Developing and evaluating polygenic risk prediction models for stratified disease prevention

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies

Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors. We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically. We develop asymptotic inferential theory for the estimator and evaluate its numerical performance via simulation studies. An application of the method is presented.

Implementation of personalized medicine in Central-Eastern Europe: pitfalls and potentials based on citizen's attitude

Objective

Next-generation sequencing is increasingly utilized worldwide as a research and diagnostic tool and is anticipated to be implemented into everyday clinical practice. Since Central-Eastern European attitude toward genetic testing, especially broad genetic testing, is not well known, we performed a survey on this issue among Hungarian participants.

Methods

A self-administered questionnaire was distributed among patients and patient relatives at our neurogenetic outpatient clinic. Members of the general population were also recruited via public media. We used chi-square testing and binary logistic regression to examine factors influencing attitude.

Results

We identified a mixed attitude toward genetic testing. Access to physician consultation positively influenced attitude. A higher self-determined genetic familiarity score associated with higher perceived genetic influence score, which in turn associated with greater willingness to participate in genetic testing. Medical professionals constituted a skeptical group.

Conclusions

We think that given the controversies and complexities of the next-generation sequencing field, the optimal clinical translation of NGS data should be performed in institutions which have the unique capability to provide interprofessional health education, transformative biomedical research, and crucial patient care. With optimization of the clinical translational process, improvement of genetic literacy may increase patient engagement and empowerment.

Relevance of the article for predictive preventive and personalized medicine

The paper highlights that in countries with relatively low-genetic literacy, a special strategy is needed to enhance the implementation of personalized medicine.

Predictive accuracy of combined genetic and environmental risk scores

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.

Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis

Background

There is a pressing need in rheumatoid arthritis (RA) to identify patients who will not respond to first-line disease-modifying anti-rheumatic drugs (DMARD). We explored whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from early RA patients before methotrexate (MTX) treatment could assist in identifying non-response to DMARD and, whether there is an association between such a signature and RA specific expression quantitative trait loci (eQTL).

Methods

We looked for the presence of a CCS in blood from early RA patients commencing MTX using chromosome conformation capture by EpiSwitch™. Using blood samples from MTX responders, non-responders and healthy controls, a custom designed biomarker discovery array was refined to a 5-marker CCS that could discriminate between responders and non-responders to MTX. We cross-validated the predictive power of the CCS by generating 150 randomized groups of 59 early RA patients (30 responders and 29 non-responders) before MTX treatment. The CCS was validated using a blinded, independent cohort of 19 early RA patients (9 responders and 10 non-responders). Last, the loci of the CCS markers were mapped to RA-specific eQTL.

Results

We identified a 5-marker CCS that could identify, at baseline, responders and non-responders to MTX. The CCS consisted of binary chromosome conformations in the genomic regions of IFNAR1, IL-21R, IL-23, CXCL13 and IL-17A. When tested on a cohort of 59 RA patients, the CCS provided a negative predictive value of 90.0% for MTX response. When tested on a blinded independent validation cohort of 19 early RA patients, the signature demonstrated a true negative response rate of 86 and a 90% sensitivity for detection of non-responders to MTX. Only conformations in responders mapped to RA-specific eQTL.

Conclusions

Here we demonstrate that detection of a CCS in blood in early RA is able to predict inadequate response to MTX with a high degree of accuracy. Our results provide a proof of principle that a priori stratification of response to MTX is possible, offering a mechanism to provide alternative treatments for non-responders to MTX earlier in the course of the disease.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1387-9) contains supplementary material, which is available to authorized users.

The surprising implications of familial association in disease risk

BACKGROUND

A wide range of diseases show some degree of clustering in families; family history is therefore an important aspect for clinicians when making risk predictions. Familial aggregation is often quantified in terms of a familial relative risk (FRR), and although at first glance this measure may seem simple and intuitive as an average risk prediction, its implications are not straightforward.

METHODS

We use two statistical models for the distribution of disease risk in a population: a dichotomous risk model that gives an intuitive understanding of the implication of a given FRR, and a continuous risk model that facilitates a more detailed computation of the inequalities in disease risk. Published estimates of FRRs are used to produce Lorenz curves and Gini indices that quantifies the inequalities in risk for a range of diseases.

RESULTS

We demonstrate that even a moderate familial association in disease risk implies a very large difference in risk between individuals in the population. We give examples of diseases for which this is likely to be true, and we further demonstrate the relationship between the point estimates of FRRs and the distribution of risk in the population.

CONCLUSIONS

The variation in risk for several severe diseases may be larger than the variation in income in many countries. The implications of familial risk estimates should be recognized by epidemiologists and clinicians.

Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts

OBJECTIVES

To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age.

DESIGN

Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa.

SETTING

Multiple institutions that were members of international PRACTICAL consortium.

PARTICIPANTS

All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men.

MAIN OUTCOME MEASURES

Prediction with hazard score of age of onset of aggressive cancer in validation set.

RESULTS

In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10-16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score.

CONCLUSIONS

Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Genetic variants demonstrating flip-flop phenomenon and breast cancer risk prediction among women of African ancestry

BACKGROUND

Few studies have evaluated the performance of existing breast cancer risk prediction models among women of African ancestry. In replication studies of genetic variants, a change in direction of the risk association is a common phenomenon. Termed flip-flop, it means that a variant is risk factor in one population but protective in another, affecting the performance of risk prediction models.

METHODS

We used data from the genome-wide association study (GWAS) of breast cancer in the African diaspora (The Root consortium), which included 3686 participants of African ancestry from Nigeria, USA, and Barbados. Polygenic risk scores (PRSs) were constructed from the published odds ratios (ORs) of four sets of susceptibility loci for breast cancer. Discrimination capacity was measured using the area under the receiver operating characteristic curve (AUC).

RESULTS

Flip-flop phenomenon was observed among 30~40% of variants across studies. Using the 34 variants with consistent directionality among previous studies, we constructed a PRS with AUC of 0.531 (95% confidence interval [CI]: 0.512-0.550), which is similar to the PRS using 93 variants and ORs from European ancestry populations (AUC = 0.525, 95% CI: 0.506-0.544). Additionally, we found the 34-variant PRS has good discriminative accuracy in women with family history of breast cancer (AUC = 0.586, 95% CI: 0.532-0.640).

CONCLUSIONS

We found that PRS based on variants identified from prior GWASs conducted in women of European and Asian ancestries did not provide a comparable degree of risk stratification for women of African ancestry. Further large-scale fine-mapping studies in African ancestry populations are desirable to discover population-specific genetic risk variants.

Genetic overlap between epilepsy and schizophrenia: Evidence from cross phenotype analysis in Hong Kong Chinese population

Epilepsy and schizophrenia are common and typical neurological or mental illness respectively, and sometimes they comorbid in the same patients, however the underlying genetic relationship between the two brain diseases is still not fully understood. To investigate the possible genetic contribution to their comorbidity, we performed polygenic risk score (PRS) analyses and genetic correlation estimation so as to identify the overall genetic overlap between the two diseases. The global schizophrenia PRS is strongly associated with schizophrenia phenotype in Hong Kong population (odds ratio = 1.7, p = 2.26E-16), and focal epilepsy PRS is moderately associated with epilepsy phenotype in Hong Kong population (odds ratio = 1.14, p = 0.013). However the disease-specific PRS can only predict its own well-matched phenotype but not the other ones (p > 0.05). This pattern is further supported by non-significant pairwise genetic correlation and insufficient statistical power for PRS association from the cross-phenotype analyses. Our study reveals there's limited shared genetic aetiology between schizophrenia and epilepsy, and thus supports a model of shared environmental factors to explain the comorbidity between the two phenotypes.

Genome-Wide Association Studies and Risk Scores for Coronary Artery Disease: Sex Biases

Phenotypic sex differences in coronary artery disease (CAD) and its risk factors have been apparent for many decades in basic and clinical research; however, whether these are also present at the gene level and thus influence genome-wide association and genetic risk prediction studies has often been ignored. From fundamental and medical standpoints, this is critically important to assess in order to fully understand the underlying genetic architecture that predisposes to CAD and better predict disease outcomes based on the interaction between genes, sex effects, and environment. In this chapter we aimed to (1) integrate the history and latest research from genome-wide association studies for CAD and clinical and genetic risk scores for prediction of CAD, (2) highlight sex-specific differences in these areas of research, and (3) discuss reasons why sex differences have often not been considered and, where present, why sex differences exist at genetic and phenotypic levels and how important they are for consideration in future research. While we find interesting examples of sex differences in effects of genetic variants on CAD, genome-wide association and genetic risk studies have typically not tested for sex-specific effects despite mounting evidence from diverse fields that these are likely very important to consider at both the genetic and phenotypic levels. In-depth testing for sex effects in large-scale genome-wide association studies that include autosomal and often excluded sex chromosomes alongside parallel improvements in resolution of sex-specific differences for risk factors and disease outcomes for CAD has the potential to substantially improve clinical and genetic risk prediction studies. Developing sex-tailored genetic risk scores as has been done recently for other disorders might be also warranted for CAD. In the era of precision medicine, this level of accuracy is essential for such a common and costly disease.

The RA-MAP Consortium: a working model for academia-industry collaboration

Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world.

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.

Prioritising the application of genomic medicine

The clinical translation of genomic sequencing is hampered by the limited information available to guide investment into those areas where genomics is well placed to deliver improved health and economic outcomes. To date, genomic medicine has achieved its greatest successes through applications to diseases that have a high genotype–phenotype correlation and high penetrance, with a near certainty that the individual will develop the condition in the presence of the genotype. It has been anticipated that genomics will play an important role in promoting population health by targeting at-risk individuals and reducing the incidence of highly prevalent, costly, complex diseases, with potential applications across screening, prevention, and treatment decisions. However, where primary or secondary prevention requires behavioural changes, there is currently very little evidence to support reduction in disease incidence. A better understanding of the relationship between genomic variation and complex diseases will be necessary before effective genomic risk identification and management of the risk of complex diseases in healthy individuals can be carried out in clinical practice. Our recommended approach is that priority for genomic testing should focus on diseases where there is strong genotype–phenotype correlation, high or certain penetrance, the effects of the disease are serious and near-term, there is the potential for prevention and/or treatment, and the net costs incurred are acceptable for the health gains achieved.

Development of multivariable models to predict change in Body Mass Index within a clinical trial population of psychotic individuals

Many antipsychotics promote weight gain, which can lead to non-compliance and relapse of psychosis. By developing models that accurately identify individuals at greater risk of weight gain, clinicians can make informed treatment decisions and target intervention measures. We examined clinical, genetic and expression data for 284 individuals with psychosis derived from a previously published randomised controlled trial (IMPACT). These data were used to develop regression and classification models predicting change in Body Mass Index (BMI) over one year. Clinical predictors included demographics, anthropometrics, cardiac and blood measures, diet and exercise, physical and mental health, medication and BMI outcome measures. We included genetic polygenic risk scores (PRS) for schizophrenia, bipolar disorder, BMI, waist-hip-ratio, insulin resistance and height, as well as gene co-expression modules generated by Weighted Gene Co-expression Network Analysis (WGCNA). The best performing predictive models for BMI and BMI gain after one year used clinical data only, which suggests expression and genetic data do not improve prediction in this cohort.

Making fair comparisons in pregnancy medication safety studies: An overview of advanced methods for confounding control

Understanding the safety of medication use during pregnancy relies on observational studies: However, confounding in observational studies poses a threat to the validity of estimates obtained from observational data. Newer methods, such as marginal structural models and propensity calibration, have emerged to deal with complex confounding problems, but these methods have seen limited uptake in the pregnancy medication literature. In this article, we provide an overview of newer advanced methods for confounding control and show how these methods are relevant for pregnancy medication safety studies.

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

Genetics of Anorexia Nervosa

PURPOSE OF REVIEW

Genetic factors contribute to the etiology of anorexia nervosa (AN). This review synthesizes the current state of knowledge about the genetic etiology of AN, provides directions for future research, and discusses clinical implications for this research.

RECENT FINDINGS

Candidate gene meta-analyses indicate serotonin genes may be involved in the genetic etiology of AN. Three genome-wide association studies have been conducted and one genome-wide significant locus was identified. Cross-disorder analyses suggest shared genetic risk between AN and several psychiatric, educational, and medical phenotypes. Much has been learned about the genetic etiology of AN over the past 3 decades. However, to fully understand the genetic architecture, we must consider all aspects including common variation, cross-disorder analysis, rare variation, copy number variation, and gene-environment interplay. Findings have important implications for the development of treatment and prevention approaches and for how AN, and psychiatric and medical diseases in general, are conceptualized.

Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models

Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models.

Genetics of Schizophrenia: Ready to Translate?

PURPOSE OF REVIEW

This is an era where we have significantly advanced the understanding of the genetic architecture of schizophrenia. In this review, we consider how this knowledge may translate into advances that will improve patient care.

RECENT FINDINGS

Large-scale genome-wide association studies (GWAS) have identified more than a hundred loci each making a small contribution to illness risk. Meta-analysis of copy number variants (CNVs) in the Psychiatric Genomics Consortium (PGC) dataset has confirmed that some variants have a moderate or large impact on risk, although these are rare in the population. Genome sequencing advances allow a much more comprehensive evaluation of genomic variation. We describe the key findings from whole exome studies to date. These studies are happening against a backdrop of growing understanding of the regulation and expression of genes and better functional tools to investigate molecular mechanisms in model systems. We provide an overview of how recent approaches in schizophrenia genetics are converging and consider how they could impact on diagnostics, the development of personalized medicine, and drug discovery.

Precisely Where Are We Going? Charting the New Terrain of Precision Prevention

In addition to genetic data, precision medicine research gathers information about three factors that modulate gene expression: lifestyles, environments, and communities. The relevant research tools-epidemiology, environmental assessment, and socioeconomic analysis-are those of public health sciences rather than molecular biology. Because these methods are designed to support inferences and interventions addressing population health, the aspirations of this research are expanding from individualized treatment toward precision prevention in public health. The purpose of this review is to explore the emerging goals and challenges of such a shift to help ensure that the genomics community and public policy makers understand the ethical issues at stake in embracing and pursuing precision prevention. Two emerging goals bear special attention in this regard: (a) public health risk reduction strategies, such as screening, and (b) the application of genomic variation studies to understand and reduce health disparities among population groups.

Barriers and Facilitating Factors for Implementation of Genetic Services: A Public Health Perspective

More than 15 years after the publication of the sequence of the human genome, the resulting changes in health care have been modest. At the same time, some promising examples in genetic services become visible, which contribute to the prevention of chronic disease such as cancer. These are discussed to identify barriers and facilitating factors for the implementation of genetic services. Examples from oncogenetics illustrate a high risk of serious disease where prevention is possible, especially in relatives. Some 5% of breast cancers and colorectal cancers are attributable to an inherited predisposition. These cancers occur at a relatively young age. DNA testing of relatives of affected patients may facilitate primary and secondary prevention. Training of non-genetic health care workers and health technology assessment are needed, as is translational research in terms of bringing genomics to health care practice while monitoring and evaluating. Stratified screening programs could include cascade screening and risk assessment based on family history. New roles and responsibilities will emerge. A clear assessment of the values implied is needed allowing to balance the pros and cons of interventions to further the responsible innovation of genetic services.

A randomized controlled trial of disclosing genetic risk information for Alzheimer disease via telephone

Purpose

Telephone disclosure of genetic test results can improve access to services. To date, studies of its impact have focused on return of Mendelian risk information, principally hereditary cancer syndromes.

Methods

In a multisite trial of Alzheimer’s disease genetic risk disclosure, asymptomatic adults were randomized to receive test results in-person or via telephone. Primary analyses examined patient outcomes 12 months after disclosure.

Results

Data from 257 participants showed that telephone disclosure occurred 7.4 days sooner and were 30% shorter, on average, than in-person disclosure (both p<0.001). Anxiety and depression scores were well below cutoffs for clinical concern across protocols. Comparing telephone and in-person disclosure protocols, 99% CIs of mean differences were within non-inferiority margins on scales assessing anxiety, depression, and test-related distress, but inconclusive about positive impact. No differences were observed on measures of recall and subjective impact. Sub-analyses supported non-inferiority on all outcomes among APOE ε4-negative participants. Sub-analyses were inconclusive for APOE ε4-positive participants, although mean anxiety and depression scores were still well below cutoffs for clinical concern.

Conclusion

Telephone disclosure of APOE results and risk for Alzheimer’s disease is generally safe and helps providers meet demands for services, even when results identify an increased risk for disease.

Posttraumatic stress disorder symptom severity is associated with reduced default mode network connectivity in individuals with elevated genetic risk for psychopathology

BACKGROUND

Accumulating evidence suggests that posttraumatic stress disorder (PTSD) is associated with disrupted default mode network (DMN) connectivity, but findings across studies have not been uniform. Individual differences in relevant genes may account for some of the reported variability in the relationship between DMN connectivity and PTSD. In this study, we investigated this possibility using genome-wide association study (GWAS) derived polygenic risk scores (PRSs) for relevant psychiatric traits. We hypothesized that the association between PTSD and DMN connectivity would be moderated by genetic risk for one or more psychiatric traits such that individuals with elevated polygenic risk for psychopathology and severe PTSD would exhibit disrupted DMN connectivity.

METHODS

Participants were 156 white, non-Hispanic veterans of the wars in Iraq and Afghanistan who were genotyped and underwent resting state functional magnetic resonance imaging and clinical assessment. PRSs for neuroticism, anxiety, major depressive disorder, and cross-disorder risk (based on five psychiatric disorders) were calculated using summary statistics from published large-scale consortia-based GWASs.

RESULTS

Cross-disorder polygenic risk influenced the relationship between DMN connectivity and PTSD symptom severity such that individuals at greater genetic risk showed a significant negative association between PTSD symptom severity and connectivity between the posterior cingulate cortex and right middle temporal gyrus. Polygenic risk for neuroticism, anxiety, and major depressive disorder did not influence DMN connectivity directly or through an interaction with PTSD.

CONCLUSIONS

Findings illustrate the potential power of genome-wide PRSs to advance understanding of the relationship between PTSD and DMN connectivity, a putative neural endophenotype of the disorder.

Risk Stratification for Second Primary Lung Cancer

Purpose This study estimated the 10-year risk of developing second primary lung cancer (SPLC) among survivors of initial primary lung cancer (IPLC) and evaluated the clinical utility of the risk prediction model for selecting eligibility criteria for screening. Methods SEER data were used to identify a population-based cohort of 20,032 participants diagnosed with IPLC between 1988 and 2003 and who survived ≥ 5 years after the initial diagnosis. We used a proportional subdistribution hazards model to estimate the 10-year risk of developing SPLC among survivors of lung cancer LC in the presence of competing risks. Considered predictors included age, sex, race, treatment, histology, stage, and extent of disease. We examined the risk-stratification ability of the prediction model and performed decision curve analysis to evaluate the clinical utility of the model by calculating its net benefit in varied risk thresholds for screening. Results Although the median 10-year risk of SPLC among survivors of LC was 8.36%, the estimated risk varied substantially (range, 0.56% to 14.3%) when stratified by age, histology, and extent of IPLC in the final prediction model. The stratification by deciles of estimated risk showed that the observed incidence of SPLC was significantly higher in the tenth-decile group (12.5%) versus the first-decile group (2.9%; P < 10^-10). The decision curve analysis yielded a range of risk thresholds (1% to 11.5%) at which the clinical net benefit of the risk model was larger than those in hypothetical all-screening or no-screening scenarios. Conclusion The risk stratification approach in SPLC can be potentially useful for identifying survivors of LC to be screened by computed tomography. More comprehensive environmental and genetic data may help enhance the predictability and stratification ability of the risk model for SPLC.

Translating genome-wide association findings into new therapeutics for psychiatry

Genome-wide association studies (GWAS) in psychiatry, once they reach sufficient sample size and power, have been enormously successful. The Psychiatric Genomics Consortium (PGC) aims for mega-analyses with sample sizes that will grow to >1 million individuals in the next 5 years. This should lead to hundreds of new findings for common genetic variants across nine psychiatric disorders studied by the PGC. The new targets discovered by GWAS have the potential to restart largely stalled psychiatric drug development pipelines, and the translation of GWAS findings into the clinic is a key aim of the recently funded phase 3 of the PGC. This is not without considerable technical challenges. These approaches complement the other main aim of GWAS studies, risk prediction approaches for improving detection, differential diagnosis, and clinical trial design. This paper outlines the motivations, technical and analytical issues, and the plans for translating PGC phase 3 findings into new therapeutics.

Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data

Genetic data consists of a wide range of marker types, including common, low-frequency, and rare variants. Multiple genetic markers and their interactions play central roles in the heritability of complex disease. In this study, we propose an algorithm that uses a stratified variable selection design by genetic architectures and interaction effects, achieved by a dataset-adaptive W-test. The polygenic sets in all strata were integrated to form a classification rule. The algorithm was applied to the Critical Assessment of Genome Interpretation 4 bipolar challenge sequencing data. The prediction accuracy was 60% using genetic markers on an independent test set. We found that epistasis among common genetic variants contributed most substantially to prediction precision. However, the sample size was not large enough to draw conclusions for the lack of predictability of low-frequency variants and their epistasis.

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.

Genetic risk prediction plays a significant role in precision medicine. Accurate prediction models could have great impact on disease prevention and treatment strategies. However, prediction accuracies for most complex diseases remain moderate mainly due to the challenges in identifying and quantifying the effects of genetic variants from millions of markers, limited access to individual-level genotype data, and lack of efficient computational methods. Up to now, most methods have been focused on predicting disease risk using data from a single trait. With the discovery of genetic correlations among many complex diseases, incorporating data of genetically correlated diseases could have the potential to increase prediction accuracy. Current statistical methods are not able to fully exploit the richness of these kinds of data to take into account the shared genetic architecture. To make use of commonly available GWAS summary statistics, we propose a novel method to address these challenges by jointly modeling genetically correlated diseases and integrating genomic functional annotations. We demonstrate the substantial improvement in accuracy in both extensive simulation studies and real data analysis of Crohn’s disease, celiac disease and type-II diabetes. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

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.

Genetics of coronary artery disease: discovery, biology and clinical translation

Coronary artery disease is the leading global cause of mortality. Long recognized to be heritable, recent advances have started to unravel the genetic architecture of the disease. Common variant association studies have linked approximately 60 genetic loci to coronary risk. Large-scale gene sequencing efforts and functional studies have facilitated a better understanding of causal risk factors, elucidated underlying biology and informed the development of new therapeutics. Moving forwards, genetic testing could enable precision medicine approaches by identifying subgroups of patients at increased risk of coronary artery disease or those with a specific driving pathophysiology in whom a therapeutic or preventive approach would be most useful.

Prediction of gene expression with cis-SNPs using mixed models and regularization methods

Background

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases.

Methods

We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data.

Results

The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R² of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R² ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R² ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks.

Conclusions

Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

Precision reproductive medicine: multigene panel testing for infertility risk assessment

The concept of precision medicine relies on a thorough understanding of the consequences of unique features of individual patients, such as environmental exposures and genetic profiles. A key component of implementing individualized care in this paradigm will be improved assessment of genetic risk. Compared with single gene tests, multigene panel testing-which has recently become commercially available for female infertility-offers the possibility of a more comprehensive and efficient risk evaluation. However, as the use of multigene panel testing for breast cancer risk has shown, this approach must be used judiciously to ensure its usefulness in a clinical setting. Key challenges which have been encountered in oncology include the interpretation of gene variants of questionable clinical effect and a lack of evidence to guide management after variants are identified. In this review, the core concepts of multigene panel testing for risk assessment are discussed, with careful attention to both its shortcomings as well as its potential for benefit in reproductive medicine.

Phenome-wide heritability analysis of the UK Biobank

Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability.

Heritability of a trait refers to the proportion of phenotypic variation that is due to genetic variation among individuals. It provides important information about the genetic basis of complex traits and indicates whether a phenotype is an appropriate target for more specific statistical and molecular genetic analyses. Recent studies have leveraged the increasingly ubiquitous genome-wide data and documented the heritability attributable to common genetic variation captured by genotyping microarrays for a wide range of human traits. However, heritability is not a fixed property of a phenotype and can vary with population-specific differences in the genetic background and environmental variation. Here, using a computationally and memory efficient heritability estimation method, we report the heritability for a large number of traits derived from the large-scale, population-based UK Biobank, and, for the first time, demonstrate the moderating effect of three major demographic variables (age, sex and socioeconomic status) on heritability estimates derived from genome-wide common genetic variation. Our study represents the first comprehensive heritability analysis across the phenotypic spectrum in the UK Biobank.

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.

Pharmacogenetics of antidepressant response: A polygenic approach

BACKGROUND

Major depressive disorder (MDD) has a high personal and socio-economic burden and >60% of patients fail to achieve remission with the first antidepressant. The biological mechanisms behind antidepressant response are only partially known but genetic factors play a relevant role. A combined predictor across genetic variants may be useful to investigate this complex trait.

METHODS

Polygenic risk scores (PRS) were used to estimate multi-allelic contribution to: 1) antidepressant efficacy; 2) its overlap with MDD and schizophrenia. We constructed PRS and tested whether these predicted symptom improvement or remission from the GENDEP study (n=736) to the STAR*D study (n=1409) and vice-versa, including the whole sample or only patients treated with escitalopram or citalopram. Using summary statistics from Psychiatric Genomics Consortium for MDD and schizophrenia, we tested whether PRS from these disorders predicted symptom improvement in GENDEP, STAR*D, and five further studies (n=3756).

RESULTS

No significant prediction of antidepressant efficacy was obtained from PRS in GENDEP/STAR*D but this analysis might have been underpowered. There was no evidence of overlap in the genetics of antidepressant response with either MDD or schizophrenia, either in individual studies or a meta-analysis. Stratifying by antidepressant did not alter the results.

DISCUSSION

We identified no significant predictive effect using PRS between pharmacogenetic studies. The genetic liability to MDD or schizophrenia did not predict response to antidepressants, suggesting differences between the genetic component of depression and treatment response. Larger or more homogeneous studies will be necessary to obtain a polygenic predictor of antidepressant response.

Risk prediction of pulmonary tuberculosis using genetic and conventional risk factors in adult Korean population

A complex interplay among host, pathogen, and environmental factors is believed to contribute to the risk of developing pulmonary tuberculosis (PTB). The lack of replication of published genome-wide association study (GWAS) findings limits the clinical utility of reported single nucleotide polymorphisms (SNPs). We conducted a GWAS using 467 PTB cases and 1,313 healthy controls obtained from two community-based cohorts in Korea. We evaluated the performance of PTB risk models based on different combinations of genetic and nongenetic factors and validated the results in an independent Korean population comprised of 179 PTB cases and 500 healthy controls. We demonstrated the polygenic nature of PTB and nongenetic factors such as age, sex, and body mass index (BMI) were strongly associated with PTB risk. None of the SNPs achieved genome-wide significance; instead, we were able to replicate the associations between PTB and ten SNPs near or in the genes, CDCA7, GBE1, GADL1, SPATA16, C6orf118, KIAA1432, DMRT2, CTR9, CCDC67, and CDH13, which may play roles in the immune and inflammatory pathways. Among the replicated SNPs, an intergenic SNP, rs9365798, located downstream of the C6orf118 gene showed the most significant association under the dominant model (OR = 1.59, 95% CI 1.32–1.92, P = 2.1×10⁻⁶). The performance of a risk model combining the effects of ten replicated SNPs and six nongenetic factors (i.e., age, sex, BMI, cigarette smoking, systolic blood pressure, and hemoglobin) were validated in the replication set (AUC = 0.80, 95% CI 0.76–0.84). The strategy of combining genetic and nongenetic risk factors ultimately resulted in better risk prediction for PTB in the adult Korean population.

Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score

BACKGROUND

Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction.

METHODS AND FINDINGS

Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer's Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10-5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer's Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer's Disease Center [NIA ADC], and Alzheimer's Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62-4.24, p = 1.0 × 10-22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10-26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran-Armitage trend test, p = 1.5 × 10-10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10-6, and Consortium to Establish a Registry for Alzheimer's Disease score for neuritic plaques, p = 6.8 × 10-6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10-6, and hippocampus, p = 7.9 × 10-5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use.

CONCLUSIONS

We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.

Risk Model for Colorectal Cancer in Spanish Population Using Environmental and Genetic Factors: Results from the MCC-Spain study

Colorectal cancer (CRC) screening of the average risk population is only indicated according to age. We aim to elaborate a model to stratify the risk of CRC by incorporating environmental data and single nucleotide polymorphisms (SNP). The MCC-Spain case-control study included 1336 CRC cases and 2744 controls. Subjects were interviewed on lifestyle factors, family and medical history. Twenty-one CRC susceptibility SNPs were genotyped. The environmental risk model, which included alcohol consumption, obesity, physical activity, red meat and vegetable consumption, and nonsteroidal anti-inflammatory drug use, contributed to CRC with an average per factor OR of 1.36 (95% CI 1.27 to 1.45). Family history of CRC contributed an OR of 2.25 (95% CI 1.87 to 2.72), and each additional SNP contributed an OR of 1.07 (95% CI 1.04 to 1.10). The risk of subjects with more than 25 risk alleles (5^th quintile) was 82% higher (OR 1.82, 95% CI 1.11 to 2.98) than subjects with less than 19 alleles (1^st quintile). This risk model, with an AUROC curve of 0.63 (95% CI 0.60 to 0.66), could be useful to stratify individuals. Environmental factors had more weight than the genetic score, which should be considered to encourage patients to achieve a healthier lifestyle.

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.

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.

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.

Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer

Nearly 50% of cancer patients undergo radiotherapy. Late radiotherapy toxicity affects quality-of-life in long-term cancer survivors and risk of side-effects in a minority limits doses prescribed to the majority of patients. Development of a test predicting risk of toxicity could benefit many cancer patients. We aimed to meta-analyze individual level data from four genome-wide association studies from prostate cancer radiotherapy cohorts including 1564 men to identify genetic markers of toxicity. Prospectively assessed two-year toxicity endpoints (urinary frequency, decreased urine stream, rectal bleeding, overall toxicity) and single nucleotide polymorphism (SNP) associations were tested using multivariable regression, adjusting for clinical and patient-related risk factors. A fixed-effects meta-analysis identified two SNPs: rs17599026 on 5q31.2 with urinary frequency (odds ratio [OR] 3.12, 95% confidence interval [CI] 2.08-4.69, p-value 4.16×10(-8)) and rs7720298 on 5p15.2 with decreased urine stream (OR 2.71, 95% CI 1.90-3.86, p-value=3.21×10(-8)). These SNPs lie within genes that are expressed in tissues adversely affected by pelvic radiotherapy including bladder, kidney, rectum and small intestine. The results show that heterogeneous radiotherapy cohorts can be combined to identify new moderate-penetrance genetic variants associated with radiotherapy toxicity. The work provides a basis for larger collaborative efforts to identify enough variants for a future test involving polygenic risk profiling.