Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Incorporating Functional Genomic Information to Enhance Polygenic Signal and Identify Variants Involved in Gene-by-Environment Interaction for Young Adult Alcohol Problems

BACKGROUND

Characterizing aggregate genetic risk for alcohol misuse and identifying variants involved in gene-by-environment (G × E) interaction effects has so far been a major challenge. We hypothesized that functional genomic information could be used to enhance detection of polygenic signal underlying alcohol misuse and to prioritize identification of single nucleotide polymorphisms (SNPs) most likely to exhibit G × E effects.

METHODS

We examined these questions in the young adult FinnTwin12 sample (n = 1,170). We used genomewide association estimates from an independent sample to derive 2 types of polygenic scores for alcohol problems in FinnTwin12. Genomewide polygenic scores included all SNPs surpassing a designated p-value threshold. DNase polygenic scores were a subset of the genomewide polygenic scores including only variants in DNase I hypersensitive sites (DHSs), which are open chromatin marks likely to index regions with a regulatory function. We conducted parallel analyses using height as a nonpsychiatric model phenotype to evaluate the consistency of effects. For the G × E analyses, we examined whether SNPs in DHSs were overrepresented among SNPs demonstrating significant G × E effects in an interaction between romantic relationship status and intoxication frequency.

RESULTS

Contrary to our expectations, we found that DNase polygenic scores were not more strongly predictive of alcohol problems than conventional polygenic scores. However, variants in DNase polygenic scores had per-SNP effects that were up to 1.4 times larger than variants in conventional polygenic scores. This same pattern of effects was also observed in supplementary analyses with height. In G × E models, SNPs in DHSs were modestly overrepresented among SNPs with significant interaction effects for intoxication frequency.

CONCLUSIONS

These findings highlight the potential utility of integrating functional genomic annotation information to increase the signal-to-noise ratio in polygenic scores and identify genetic variants that may be most susceptible to environmental modification.

Polygenic risk for alcohol consumption and its association with alcohol-related phenotypes: Do stress and life satisfaction moderate these relationships?

BACKGROUND

Genetic and environmental factors contribute about equally to alcohol-related phenotypes in adulthood. In the present study, we examined whether more stress at home or low satisfaction with life might be associated with heavier drinking or more alcohol-related problems in individuals with a high genetic susceptibility to alcohol use.

METHODS

Information on polygenic scores and drinking behavior was available in 6705 adults (65% female; 18-83 years) registered with the Netherlands Twin Register. Polygenic risk scores (PRSs) were constructed for all subjects based on the summary statistics of a large genome-wide association meta-analysis on alcohol consumption (grams per day). Outcome measures were quantity of alcohol consumption and alcohol-related problems assessed with the Alcohol Use Disorders Identification Test (AUDIT). Stress at home and life satisfaction were moderating variables whose significance was tested by Generalized Estimating Equation analyses taking familial relatedness, age and sex into account.

RESULTS

PRSs for alcohol were significantly associated with quantity of alcohol consumption and alcohol-related problems in the past year (R²=0.11% and 0.10% respectively). Participants who reported to have experienced more stress in the past year and lower life satisfaction, scored higher on alcohol-related problems (R²=0.27% and 0.29 respectively), but not on alcohol consumption. Stress and life satisfaction did not moderate the association between PRSs and the alcohol outcome measures.

CONCLUSIONS

There were significant main effects of polygenic scores and of stress and life satisfaction on drinking behavior, but there was no support for PRS-by-stress or PRS-by-life satisfaction interactions on alcohol consumption and alcohol-related problems.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

Multi-trait analysis of genome-wide association summary statistics using MTAG

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Genetic basis of human congenital anomalies of the kidney and urinary tract

The clinical spectrum of congenital anomalies of the kidney and urinary tract (CAKUT) encompasses a common birth defect in humans that has significant impact on long-term patient survival. Overall, data indicate that approximately 20% of patients may have a genetic disorder that is usually not detected based on standard clinical evaluation, implicating many different mutational mechanisms and pathogenic pathways. In particular, 10% to 15% of CAKUT patients harbor an unsuspected genomic disorder that increases risk of neurocognitive impairment and whose early recognition can impact clinical care. The emergence of high-throughput genomic technologies is expected to provide insight into the common and rare genetic determinants of diseases and offer opportunities for early diagnosis with genetic testing.

Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials

Human genetic research in the past decade has generated a wealth of data from the genome-wide association scan era, much of which is catalogued and freely available. These data will typically test the relationship between a single nucleotide variant or polymorphism (SNP) and some outcome, disease, or trait. Ongoing investigations will yield a similar wealth of data regarding epigenetic phenomena. These data will typically test the relationship between DNA methylation at a single genomic location/region and some outcome. Most of these findings will be the result of cross-sectional investigations typically using ascertained cases and controls. Consequently, most methodological consideration focuses on methods appropriate for simple case-control comparisons. It is expected that a growing number of investigators with longitudinal experimental prevention or intervention cohorts will also measure genetic and epigenetic indicators as part of their investigations, harvesting the wealth of information generated by the genome-wide association study (GWAS) era to allow for targeted hypothesis testing in the next generation of prevention and intervention trials. Herein, we discuss appropriate quality control and statistical modelling of genetic, polygenic, and epigenetic measures in longitudinal models. We specifically discuss quality control, population stratification, genotype imputation, pathway approaches, and proper modelling of an interaction between a specific genetic variant and an environment variable (GxE interaction).

Towards diversity in genomics: The emergence of neurogenomics in Africa?

There is a high burden of mental and neurological disorders in Africa. Nevertheless, there appears to be an under-representation of African ancestry populations in large-scale genomic studies. Here, we evaluated the extent of under-representation of Africans in neurogenomic studies in the GWAS Catalog. We found 569 neurogenomic studies, of which 88.9% were exclusively focused on people with European ancestry and the remaining 11.1% having African ancestry cases included. In terms of population, only 1.2% of the total populations involved in these 569 GWAS studies were of African descent. Further, most of the individuals in the African ancestry category were identified to be African-Americans/Afro-Caribbeans, highlighting the huge under-representation of homogenous African populations in large-scale neurogenomic studies. Efforts geared at establishing strong collaborative ties with European/American researchers, maintaining freely accessible biobanks and establishing comprehensive African genome data repositories to track African genome variations are critical for propelling neurogenomics/precision medicine in Africa.

Exploring the predictive power of polygenic scores derived from genome-wide association studies: a study of 10 complex traits

Motivation

It is hoped that advances in our knowledge in disease genomics will contribute to personalized medicine such as individualized preventive strategies or early diagnoses of diseases. With the growth of genome-wide association studies (GWAS) in the past decade, how far have we reached this goal? In this study we explored the predictive ability of polygenic risk scores (PRSs) derived from GWAS for a range of complex disease and traits.

Results

We first proposed a new approach to evaluate predictive performances of PRS at arbitrary P -value thresholds. The method was based on corrected estimates of effect sizes, accounting for possible false positives and selection bias. This approach requires no distributional assumptions and only requires summary statistics as input. The validity of the approach was verified in simulations. We explored the predictive power of PRS for ten complex traits, including type 2 diabetes (DM), coronary artery disease (CAD), triglycerides, high- and low-density lipoprotein, total cholesterol, schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder and anxiety disorders. We found that the predictive ability of PRS for CAD and DM were modest (best AUC = 0.608 and 0.607) while for lipid traits the prediction R-squared ranged from 16.1 to 29.8%. For psychiatric disorders, the predictive power for SCZ was estimated to be the highest (best AUC 0.820), followed by BD. Predictive performance of other psychiatric disorders ranged from 0.543 to 0.585. Psychiatric traits tend to have more gradual rise in AUC when significance thresholds increase and achieve the best predictive power at higher P -values than cardiometabolic traits.

Contact

hcso@cuhk.edu.hk ; pcsham@hku.hk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multiethnic polygenic risk scores improve risk prediction in diverse populations

Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff  = 40k) and Latino training data in small sample size (Neff  = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2  = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff  = 40k) and South Asian (Neff  = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.

The Effect of Vietnam-Era Conscription and Genetic Potential for Educational Attainment on Schooling Outcomes

This study examines whether draft lottery estimates of the causal effects of Vietnam-era military service on schooling vary by an individual's genetic propensity toward educational attainment. To capture the complex genetic architecture that underlies the bio-developmental pathways, behavioral traits and evoked environments associated with educational attainment, we construct polygenic scores (PGS) for respondents in the Health and Retirement Study (HRS) that aggregate thousands of individual loci across the human genome and weight them by effect sizes derived from a recent genome-wide association study (GWAS) of years of education. Our findings suggest veterans with below average PGSs for educational attainment may have completed fewer years of schooling than comparable non-veterans. On the other hand, we do not find any difference in the educational attainment of veterans and non-veterans with above average PGSs. Results indicate that public policies and exogenous environments may induce heterogeneous treatment effects by genetic disposition.

Genetic Association of Major Depression With Atypical Features and Obesity-Related Immunometabolic Dysregulations

IMPORTANCE

The association between major depressive disorder (MDD) and obesity may stem from shared immunometabolic mechanisms particularly evident in MDD with atypical features, characterized by increased appetite and/or weight (A/W) during an active episode.

OBJECTIVE

To determine whether subgroups of patients with MDD stratified according to the A/W criterion had a different degree of genetic overlap with obesity-related traits (body mass index [BMI] and levels of C-reactive protein [CRP] and leptin).

DESIGN, SETTING, AND PATIENTS

This multicenter study assembled genome-wide genotypic and phenotypic measures from 14 data sets of the Psychiatric Genomics Consortium. Data sets were drawn from case-control, cohort, and population-based studies, including 26 628 participants with established psychiatric diagnoses and genome-wide genotype data. Data on BMI were available for 15 237 participants. Data were retrieved and analyzed from September 28, 2015, through May 20, 2017.

MAIN OUTCOMES AND MEASURES

Lifetime DSM-IV MDD was diagnosed using structured diagnostic instruments. Patients with MDD were stratified into subgroups according to change in the DSM-IV A/W symptoms as decreased or increased.

RESULTS

Data included 11 837 participants with MDD and 14 791 control individuals, for a total of 26 628 participants (59.1% female and 40.9% male). Among participants with MDD, 5347 (45.2%) were classified in the decreased A/W and 1871 (15.8%) in the increased A/W subgroups. Common genetic variants explained approximately 10% of the heritability in the 2 subgroups. The increased A/W subgroup showed a strong and positive genetic correlation (SE) with BMI (0.53 [0.15]; P = 6.3 × 10-4), whereas the decreased A/W subgroup showed an inverse correlation (-0.28 [0.14]; P = .06). Furthermore, the decreased A/W subgroup had a higher polygenic risk for increased BMI (odds ratio [OR], 1.18; 95% CI, 1.12-1.25; P = 1.6 × 10-10) and levels of CRP (OR, 1.08; 95% CI, 1.02-1.13; P = 7.3 × 10-3) and leptin (OR, 1.09; 95% CI, 1.06-1.12; P = 1.7 × 10-3).

CONCLUSIONS AND RELEVANCE

The phenotypic associations between atypical depressive symptoms and obesity-related traits may arise from shared pathophysiologic mechanisms in patients with MDD. Development of treatments effectively targeting immunometabolic dysregulations may benefit patients with depression and obesity, both syndromes with important disability.

Validating Online Measures of Cognitive Ability in Genes for Good, a Genetic Study of Health and Behavior

Genetic association studies routinely require many thousands of participants to achieve sufficient power, yet accumulation of large well-assessed samples is costly. We describe here an effort to efficiently measure cognitive ability and personality in an online genetic study, Genes for Good. We report on the first 21,550 participants with relevant phenotypic data, 7,458 of whom have been genotyped genome-wide. Measures of crystallized and fluid intelligence reflected a two-dimensional latent ability space, with items demonstrating adequate item-level characteristics. The Big Five Inventory questionnaire revealed the expected five-factor model of personality. Cognitive measures predicted educational attainment over and above personality characteristics, as expected. We found that a genome-wide polygenic score of educational attainment predicted educational level, accounting for 4%, 4%, and 2.7% of the variance in educational attainment, verbal reasoning, and spatial reasoning, respectively. In summary, the online cognitive measures in Genes for Good appear to perform adequately and demonstrate expected associations with personality, education, and an education-based polygenic score. Results indicate that online cognitive assessment is one avenue to accumulate large samples of individuals for genetic research of cognitive ability.

Sample size requirements for learning to classify with high-dimensional biomarker panels

A common problem in biomedical research is to calculate the sample size required to learn a classifier using a (possibly high-dimensional) panel of biomarkers. This paper describes a simple method based on a Gaussian approximation for calculating the predictive performance of the learned classifier given the size of the biomarker panel, the size of the training sample, and the optimal predictive performance (expressed as C-statistic Copt) of the biomarker panel that could be obtained if a training sample of unlimited size were available. Under the assumption that the biomarker effect sizes have the same correlation structure as the biomarkers, the required sample size does not depend upon these correlations, but only upon Copt and upon the sparsity of the distribution of effect sizes, defined by the proportion of biomarkers that have nonzero effects. To learn a classifier that extracts 80% of the predictive information, the required case sample size varies from about 0.1 cases per variable for a panel with Copt=0.9 and a sparse distribution of effect sizes (such that 1% of biomarkers have nonzero effect sizes) to nine cases per variable for a panel with Copt=0.75 and a diffuse distribution of effect sizes.

Neuroimaging genomics in psychiatry-a translational approach

Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.

Opportunities for an enhanced integration of neuroscience and genomics

Neuroimaging and genetics are two rapidly expanding fields of research. Thoughtful integration of these areas is critical for ongoing large-scale research into the genetic mechanisms underlying brain structure, function, and development. Neuroimaging genetics has been slow to evolve relative to psychiatric genetics research, and some may be unaware that new statistical methods allow for the genomic analysis of more modestly-sized imaging samples. We present a broad overview of the extant imaging genetics literature, provide an interpretation of the major problems surrounding the integration of neuroimaging and genetics, discuss the influence and impact of genetics consortia, and suggest statistical genetic analyses that expand the repertoire of imaging researchers amassing rich behavioral data in modestly-sized samples. Specific attention is paid to the creative use of polygenic risk scoring in imaging genetic analyses, with primers on the most current risk scoring applications.

Genetic Overlap Between Schizophrenia and Developmental Psychopathology: Longitudinal and Multivariate Polygenic Risk Prediction of Common Psychiatric Traits During Development

Background

Several nonpsychotic psychiatric disorders in childhood and adolescence can precede the onset of schizophrenia, but the etiology of this relationship remains unclear. We investigated to what extent the association between schizophrenia and psychiatric disorders in childhood is explained by correlated genetic risk factors.

Methods

Polygenic risk scores (PRS), reflecting an individual's genetic risk for schizophrenia, were constructed for 2588 children from the Netherlands Twin Register (NTR) and 6127 from the Avon Longitudinal Study of Parents And Children (ALSPAC). The associations between schizophrenia PRS and measures of anxiety, depression, attention deficit hyperactivity disorder (ADHD), and oppositional defiant disorder/conduct disorder (ODD/CD) were estimated at age 7, 10, 12/13, and 15 years in the 2 cohorts. Results were then meta-analyzed, and a meta-regression analysis was performed to test differences in effects sizes over, age and disorders.

Results

Schizophrenia PRS were associated with childhood and adolescent psychopathology. Meta-regression analysis showed differences in the associations over disorders, with the strongest association with childhood and adolescent depression and a weaker association for ODD/CD at age 7. The associations increased with age and this increase was steepest for ADHD and ODD/CD. Genetic correlations varied between 0.10 and 0.25.

Conclusion

By optimally using longitudinal data across diagnoses in a multivariate meta-analysis this study sheds light on the development of childhood disorders into severe adult psychiatric disorders. The results are consistent with a common genetic etiology of schizophrenia and developmental psychopathology as well as with a stronger shared genetic etiology between schizophrenia and adolescent onset psychopathology.

Widespread covariation of early environmental exposures and trait-associated polygenic variation

Although gene-environment correlation is recognized and investigated by family studies and recently by SNP-heritability studies, the possibility that genetic effects on traits capture environmental risk factors or protective factors has been neglected by polygenic prediction models. We investigated covariation between trait-associated polygenic variation identified by genome-wide association studies (GWASs) and specific environmental exposures, controlling for overall genetic relatedness using a genomic relatedness matrix restricted maximum-likelihood model. In a UK-representative sample (n = 6,710), we find widespread covariation between offspring trait-associated polygenic variation and parental behavior and characteristics relevant to children's developmental outcomes-independently of population stratification. For instance, offspring genetic risk for schizophrenia was associated with paternal age (R² = 0.002; P = 1e-04), and offspring education-associated variation was associated with variance in breastfeeding (R² = 0.021; P = 7e-30), maternal smoking during pregnancy (R² = 0.008; P = 5e-13), parental smacking (R² = 0.01; P = 4e-15), household income (R² = 0.032; P = 1e-22), watching television (R² = 0.034; P = 5e-47), and maternal education (R² = 0.065; P = 3e-96). Education-associated polygenic variation also captured covariation between environmental exposures and children's inattention/hyperactivity, conduct problems, and educational achievement. The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a "Regression with Summary Statistics" (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.

Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies

During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research.

A machine-learning heuristic to improve gene score prediction of polygenic traits

Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R² of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N = 130 K; 1.98 M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p < 2.2 × 10⁻¹⁶) and BMI (p < 1.57 × 10⁻⁴), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N = 8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.

Polygenic Risk Score for Schizophrenia and Treatment-Resistant Schizophrenia

Treatment-resistant schizophrenia (TRS) affects around one-third of individuals with schizophrenia. Although a number of sociodemographic and clinical predictors of TRS have been identified, data on the genetic risk of TRS are sparse. We aimed to investigate the association between a polygenic risk score for schizophrenia and treatment resistance in patients with schizophrenia. We conducted a nationwide, population-based follow-up study among all Danish individuals born after 1981 and with an incident diagnosis of schizophrenia between 1999 and 2007. Based on genome-wide data polygenic risk scores for schizophrenia were calculated in 862 individuals with schizophrenia. TRS was defined as either clozapine initiation or at least 2 periods of different antipsychotic monotherapies and still being hospitalized. We estimated hazard rate ratios (HRs) for TRS in relation to the polygenic risk score while adjusting for population stratification, age, sex, geographical area at birth, clinical treatment setting, psychiatric comorbidity, and calendar year. Among the 862 individuals with schizophrenia, 181 (21.0%) met criteria for TRS during 4674 person-years of follow-up. We found no significant association between the polygenic risk score and TRS, adjusted HR = 1.13 (95% CI: 0.95-1.35). Based on these results, the use of the polygenic risk score for schizophrenia to identify individuals with TRS is at present inadequate to be of clinical utility at the individual patient level. Future research should include larger genetic samples in combination with non-genetic markers. Moreover, a TRS-specific developed polygenic risk score would be of great interest towards early prediction of TRS.

Polygenic risk for schizophrenia and neurocognitive performance in patients with schizophrenia

Both neurocognitive deficits and schizophrenia are highly heritable. Genetic overlap between neurocognitive deficits and schizophrenia has been observed in both the general population and in the clinical samples. This study aimed to examine if the polygenic architecture of susceptibility to schizophrenia modified neurocognitive performance in schizophrenia patients. Schizophrenia polygenic risk scores (PRSs) were first derived from the Psychiatric Genomics Consortium (PGC) on schizophrenia, and then the scores were calculated in our independent sample of 1130 schizophrenia trios, who had PsychChip data and were part of the Schizophrenia Families from Taiwan project. Pseudocontrols generated from the nontransmitted parental alleles of the parents in these trios were compared with alleles in schizophrenia patients in assessing the replicability of PGC-derived susceptibility variants. Schizophrenia PRS at the P-value threshold (PT) of 0.1 explained 0.2% in the variance of disease status in this Han-Taiwanese samples, and the score itself had a P-value 0.05 for the association test with the disorder. Each patient underwent neurocognitive evaluation on sustained attention using the continuous performance test and executive function using the Wisconsin Card Sorting Test. We applied a structural equation model to construct the neurocognitive latent variable estimated from multiple measured indices in these 2 tests, and then tested the association between the PRS and the neurocognitive latent variable. Higher schizophrenia PRS generated at the PT of 0.1 was significantly associated with poorer neurocognitive performance with explained variance 0.5%. Our findings indicated that schizophrenia susceptibility variants modify the neurocognitive performance in schizophrenia patients.

Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential

Imaging genetics and genomics research has begun to provide insight into the molecular and genetic architecture of neural phenotypes and the neural mechanisms through which genetic risk for psychopathology may emerge. As it approaches its third decade, imaging genetics is confronted by many challenges, including the proliferation of studies using small sample sizes and diverse designs, limited replication, problems with harmonization of neural phenotypes for meta-analysis, unclear mechanisms, and evidence that effect sizes may be more modest than originally posited, with increasing evidence of polygenicity. These concerns have encouraged the field to grow in many new directions, including the development of consortia and large-scale data collection projects and the use of novel methods (e.g., polygenic approaches, machine learning) that enhance the quality of imaging genetic studies but also introduce new challenges. We critically review progress in imaging genetics and offer suggestions and highlight potential pitfalls of novel approaches. Ultimately, the strength of imaging genetics and genomics lies in their translational and integrative potential with other research approaches (e.g., nonhuman animal models, psychiatric genetics, pharmacologic challenge) to elucidate brain-based pathways that give rise to the vast individual differences in behavior as well as risk for psychopathology.

Associations between subjective well-being and subcortical brain volumes

To study the underpinnings of individual differences in subjective well-being (SWB), we tested for associations of SWB with subcortical brain volumes in a dataset of 724 twins and siblings. For significant SWB-brain associations we probed for causal pathways using Mendelian Randomization (MR) and estimated genetic and environmental contributions from twin modeling. Another independent measure of genetic correlation was obtained from linkage disequilibrium (LD) score regression on published genome-wide association summary statistics. Our results indicated associations of SWB with hippocampal volumes but not with volumes of the basal ganglia, thalamus, amygdala, or nucleus accumbens. The SWB-hippocampus relations were nonlinear and characterized by lower SWB in subjects with relatively smaller hippocampal volumes compared to subjects with medium and higher hippocampal volumes. MR provided no evidence for an SWB to hippocampal volume or hippocampal volume to SWB pathway. This was in line with twin modeling and LD-score regression results which indicated non-significant genetic correlations. We conclude that low SWB is associated with smaller hippocampal volume, but that genes are not very important in this relationship. Instead other etiological factors, such as exposure to stress and stress hormones, may exert detrimental effects on SWB and the hippocampus to bring about the observed association.

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence

Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10-8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10-6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10-6). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10-29). These findings provide new insight into the genetic architecture of intelligence.

Reproductive fitness and genetic risk of psychiatric disorders in the general population

The persistence of common, heritable psychiatric disorders that reduce reproductive fitness is an evolutionary paradox. Here, we investigate the selection pressures on sequence variants that predispose to schizophrenia, autism, bipolar disorder, major depression and attention deficit hyperactivity disorder (ADHD) using genomic data from 150,656 Icelanders, excluding those diagnosed with these psychiatric diseases. Polygenic risk of autism and ADHD is associated with number of children. Higher polygenic risk of autism is associated with fewer children and older age at first child whereas higher polygenic risk of ADHD is associated with having more children. We find no evidence for a selective advantage of a high polygenic risk of schizophrenia or bipolar disorder. Rare copy-number variants conferring moderate to high risk of psychiatric illness are associated with having fewer children and are under stronger negative selection pressure than common sequence variants.

Why genetic variants that confer risk for psychiatric disorders persist in the genome is an evolutionary conundrum. Here, Mullins et al. report association of polygenic risk for autism with having fewer children and polygenic risk for ADHD with higher reproductive fitness.

Leveraging functional annotations in genetic risk prediction for human complex diseases

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.

Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction

Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS) in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

Thought Disorder in Schizophrenia and Bipolar Disorder Probands, Their Relatives, and Nonpsychiatric Controls

Thought disorder (TD) has long been associated with schizophrenia (SZ) and is now widely recognized as a symptom of mania and other psychotic disorders as well. Previous studies have suggested that the TD found in the clinically unaffected relatives of SZ, schizoaffective and bipolar probands is qualitatively similar to that found in the probands themselves. Here, we examine which quantitative measures of TD optimize the distinction between patients with diagnoses of SZ and bipolar disorder with psychotic features (BP) from nonpsychiatric controls (NC) and from each other. In addition, we investigate whether these same TD measures also distinguish their respective clinically unaffected relatives (RelSZ, RelBP) from controls as well as from each other. We find that deviant verbalizations are significantly associated with SZ and are co-familial in clinically unaffected RelSZ, but are dissociated from, and are not co-familial for, BP disorder. In contrast, combinatory thinking was nonspecifically associated with psychosis, but did not aggregate in either group of relatives. These results provide further support for the usefulness of TD for identifying potential non-penetrant carriers of SZ-risk genes, in turn enhancing the power of genetic analyses. These findings also suggest that further refinement of the TD phenotype may be needed in order to be suitable for use in genetic studies of bipolar disorder.

Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity

In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin-melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene-environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput '-omic' data can be integrated in order to get closer to the new age of personalized medicine.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Polygenic risk assessment reveals pleiotropy between sarcoidosis and inflammatory disorders in the context of genetic ancestry

Sarcoidosis is a complex disease of unknown etiology characterized by the presence of granulomatous inflammation. Though various immune system pathways have been implicated in disease, the relationship between the genetic determinants of sarcoidosis and other inflammatory disorders has not been characterized. Herein, we examined the degree of genetic pleiotropy common to sarcoidosis and other inflammatory disorders to identify shared pathways and disease systems pertinent to sarcoidosis onset. To achieve this, we quantify the association of common variant polygenic risk scores from nine complex inflammatory disorders with sarcoidosis risk. Enrichment analyses of genes implicated in pleiotropic associations were further used to elucidate candidate pathways. In European-Americans, we identify significant pleiotropy between risk of sarcoidosis and risk of asthma (R²=2.03%; p=8.89×10⁻⁹), celiac disease (R²=2.03%; p=8.21×10⁻⁹), primary biliary cirrhosis (R²=2.43%; p=2.01×10⁻¹⁰), and rheumatoid arthritis (R²=4.32%; p=2.50×10⁻¹⁷). These associations validate in African Americans only after accounting for the proportion of genome-wide European ancestry, where we demonstrate similar effects of polygenic risk for African-Americans with the highest levels of European ancestry. Variants and genes implicated in European-American pleiotropic associations were enriched for pathways involving interleukin-12, interleukin-27, and cell adhesion molecules, corroborating the hypothesized immunopathogenesis of disease.

Sequence variant at 8q24.21 associates with sciatica caused by lumbar disc herniation

Lumbar disc herniation (LDH) is common and often debilitating. Microdiscectomy of herniated lumbar discs (LDHsurg) is performed on the most severe cases to resolve the resulting sciatica. Here we perform a genome-wide association study on 4,748 LDHsurg cases and 282,590 population controls and discover 37 highly correlated markers associating with LDHsurg at 8q24.21 (between CCDC26 and GSDMC), represented by rs6651255[C] (OR=0.81; P=5.6 × 10⁻¹²) with a stronger effect among younger patients than older. As rs6651255[C] also associates with height, we performed a Mendelian randomization analysis using height polygenic risk scores as instruments to estimate the effect of height on LDHsurg risk, and found that the marker's association with LDHsurg is much greater than predicted by its effect on height. In light of presented findings, we speculate that the effect of rs6651255 on LDHsurg is driven by susceptibility to developing severe and persistent sciatica upon LDH.

Lumbar disc herniation (LDH) can cause persistent sciatica, and in some cases surgery is required to relieve symptoms. Here, the authors carry out a genome-wide association study using microdiscectomy as an indicator of severe LDH, and find a locus on chromosome 8 associated with this condition.

Using Clinical Characteristics to Identify Which Patients With Major Depressive Disorder Have a Higher Genetic Load for Three Psychiatric Disorders

BACKGROUND

Limited successes of gene finding for major depressive disorder (MDD) may be partly due to phenotypic heterogeneity. We tested whether the genetic load for MDD, bipolar disorder, and schizophrenia (SCZ) is increased in phenotypically more homogenous MDD patients identified by specific clinical characteristics.

METHODS

Patients (n = 1539) with a DSM-IV MDD diagnosis and control subjects (n = 1792) were from two large cohort studies (Netherlands Study of Depression and Anxiety and Netherlands Twin Register). Genomic profile risk scores (GPRSs) for MDD, bipolar disorder, and SCZ were based on meta-analysis results of the Psychiatric Genomics Consortium. Regression analyses (adjusted for year of birth, sex, three principal components) examined the association between GPRSs with characteristics and GPRSs with MDD subgroups stratified according to the most relevant characteristics. The proportion of liability variance explained by GPRSs for each MDD subgroup was estimated.

RESULTS

GPRS-MDD explained 1.0% (p = 4.19e^-09) of MDD variance, and 1.5% (p = 4.23e^-09) for MDD endorsing nine DSM symptoms. GPRS-bipolar disorder explained 0.6% (p = 2.97e^-05) of MDD variance and 1.1% (p = 1.30e^-05) for MDD with age at onset <18 years. GPRS-SCZ explained 2.0% (p = 6.15e^-16) of MDD variance, 2.6% (p = 2.88e^-10) for MDD with higher symptom severity, and 2.3% (p = 2.26e^-13) for MDD endorsing nine DSM symptoms. An independent sample replicated the same pattern of stronger associations between cases with more DSM symptoms, as compared to overall MDD, and GPRS-SCZ.

CONCLUSIONS

MDD patients with early age at onset and higher symptom severity have an increased genetic risk for three major psychiatric disorders, suggesting that it is useful to create phenotypically more homogenous groups when searching for genes associated with MDD.

Short communication: Genetic association between schizophrenia and cannabis use

BACKGROUND AND AIM

Previous studies have shown a relationship between schizophrenia and cannabis use. As both traits are substantially heritable, a shared genetic liability could explain the association. We use two recently developed genomics methods to investigate the genetic overlap between schizophrenia and cannabis use.

METHODS

Firstly, polygenic risk scores for schizophrenia were created based on summary statistics from the largest schizophrenia genome-wide association (GWA) meta-analysis to date. We analysed the association between these schizophrenia polygenic scores and multiple cannabis use phenotypes (lifetime use, regular use, age at initiation, and quantity and frequency of use) in a sample of 6,931 individuals. Secondly, we applied LD-score regression to the GWA summary statistics of schizophrenia and lifetime cannabis use to calculate the genome-wide genetic correlation.

RESULTS

Polygenic risk scores for schizophrenia were significantly (α<0.05) associated with five of the eight cannabis use phenotypes, including lifetime use, regular use, and quantity of use, with risk scores explaining up to 0.5% of the variance. Associations were not significant for age at initiation of use and two measures of frequency of use analyzed in lifetime users only, potentially because of reduced power due to a smaller sample size. The LD-score regression revealed a significant genetic correlation of r_g=0.22 (SE=0.07, p=0.003) between schizophrenia and lifetime cannabis use.

CONCLUSIONS

Common genetic variants underlying schizophrenia and lifetime cannabis use are partly overlapping. Individuals with a stronger genetic predisposition to schizophrenia are more likely to initiate cannabis use, use cannabis more regularly, and consume more cannabis over their lifetime.

Dissecting the genetics of complex traits using summary association statistics

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Improving polygenic risk prediction from summary statistics by an empirical Bayes approach

Polygenic risk scores (PRS) from genome-wide association studies (GWAS) are increasingly used to predict disease risks. However some included variants could be false positives and the raw estimates of effect sizes from them may be subject to selection bias. In addition, the standard PRS approach requires testing over a range of p-value thresholds, which are often chosen arbitrarily. The prediction error estimated from the optimized threshold may also be subject to an optimistic bias. To improve genomic risk prediction, we proposed new empirical Bayes approaches to recover the underlying effect sizes and used them as weights to construct PRS. We applied the new PRS to twelve cardio-metabolic traits in the Northern Finland Birth Cohort and demonstrated improvements in predictive power (in R²) when compared to standard PRS at the best p-value threshold. Importantly, for eleven out of the twelve traits studied, the predictive performance from the entire set of genome-wide markers outperformed the best R² from standard PRS at optimal p-value thresholds. Our proposed methodology essentially enables an automatic PRS weighting scheme without the need of choosing tuning parameters. The new method also performed satisfactorily in simulations. It is computationally simple and does not require assumptions on the effect size distributions.

Selection against variants in the genome associated with educational attainment

Epidemiological and genetic association studies show that genetics play an important role in the attainment of education. Here, we investigate the effect of this genetic component on the reproductive history of 109,120 Icelanders and the consequent impact on the gene pool over time. We show that an educational attainment polygenic score, POLY_EDU, constructed from results of a recent study is associated with delayed reproduction (P < 10^-100) and fewer children overall. The effect is stronger for women and remains highly significant after adjusting for educational attainment. Based on 129,808 Icelanders born between 1910 and 1990, we find that the average POLY_EDU has been declining at a rate of ∼0.010 standard units per decade, which is substantial on an evolutionary timescale. Most importantly, because POLY_EDU only captures a fraction of the overall underlying genetic component the latter could be declining at a rate that is two to three times faster.

Endophenotype best practices

This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.

Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

Epigenetic and genetic components of height regulation

Adult height is a highly heritable trait. Here we identified 31.6 million sequence variants by whole-genome sequencing of 8,453 Icelanders and tested them for association with adult height by imputing them into 88,835 Icelanders. Here we discovered 13 novel height associations by testing four different models including parent-of-origin (|β|=0.4-10.6 cm). The minor alleles of three parent-of-origin signals associate with less height only when inherited from the father and are located within imprinted regions (IGF2-H19 and DLK1-MEG3). We also examined the association of these sequence variants in a set of 12,645 Icelanders with birth length measurements. Two of the novel variants, (IGF2-H19 and TET1), show significant association with both adult height and birth length, indicating a role in early growth regulation. Among the parent-of-origin signals, we observed opposing parental effects raising questions about underlying mechanisms. These findings demonstrate that common variations affect human growth by parental imprinting.

Integrative genetic risk prediction using non-parametric empirical Bayes classification

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa.

SNP-based heritability estimates of the personality dimensions and polygenic prediction of both neuroticism and major depression: findings from CONVERGE

Biometrical genetic studies suggest that the personality dimensions, including neuroticism, are moderately heritable (~0.4 to 0.6). Quantitative analyses that aggregate the effects of many common variants have recently further informed genetic research on European samples. However, there has been limited research to date on non-European populations. This study examined the personality dimensions in a large sample of Han Chinese descent (N=10 064) from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology study, aimed at identifying genetic risk factors for recurrent major depression among a rigorously ascertained cohort. Heritability of neuroticism as measured by the Eysenck Personality Questionnaire (EPQ) was estimated to be low but statistically significant at 10% (s.e.=0.03, P=0.0001). In addition to EPQ, neuroticism based on a three-factor model, data for the Big Five (BF) personality dimensions (neuroticism, openness, conscientiousness, extraversion and agreeableness) measured by the Big Five Inventory were available for controls (n=5596). Heritability estimates of the BF were not statistically significant despite high power (>0.85) to detect heritabilities of 0.10. Polygenic risk scores constructed by best linear unbiased prediction weights applied to split-half samples failed to significantly predict any of the personality traits, but polygenic risk for neuroticism, calculated with LDpred and based on predictive variants previously identified from European populations (N=171 911), significantly predicted major depressive disorder case-control status (P=0.0004) after false discovery rate correction. The scores also significantly predicted EPQ neuroticism (P=6.3 × 10-6). Factor analytic results of the measures indicated that any differences in heritabilities across samples may be due to genetic variation or variation in haplotype structure between samples, rather than measurement non-invariance. Findings demonstrate that neuroticism can be significantly predicted across ancestry, and highlight the importance of studying polygenic contributions to personality in non-European populations.