Robert Maier

Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.

Genetic Basis of Complex Genetic Disease: The Contribution of Disease Heterogeneity to Missing Heritability

The genetic basis of complex genetic disease can be quantified by heritability, which is an estimate of the relative importance of genetic and non-genetic factors in contributing to differences between individuals for any given trait. Heritability is estimated from phenotypic records in data sets of families and represents contributions from genetic variants across the frequency spectrum and of any kind and function. Advances in technology allow direct interrogation of some kinds of DNA variants. Specific DNA variants identified in the era of genome-wide association studies explain only a fraction of the heritability estimated from family studies, as do less common variants identified through whole exome sequencing. If true effect sizes of risk variants are small, studies to date may be underpowered to detect individual risk variants; but the studies may be well-powered to detect the total contribution from common risk variants, and this has explained some of the missing heritability. Here we review explanations for the so-called “still-missing heritability” and focus particularly on the issue of genetic heterogeneity.

Causal associations between risk factors and common diseases inferred from GWAS summary data

Health risk factors such as body mass index (BMI) and serum cholesterol are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. We develop and apply a method (called GSMR) that performs a multi-SNP Mendelian randomization analysis using summary-level data from genome-wide association studies to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height, and years of schooling (EduYears) with common diseases (sample sizes of up to 405,072). We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimer’s disease, and bidirectional associations with opposite effects (e.g., higher BMI increases the risk of T2D but the effect of T2D on BMI is negative).Genetic methods are useful to test whether risk factors are causal for or consequence of disease. Here, Zhu et al. develop a generalized summary-based Mendelian Randomization (GSMR) method which uses summary-level data from GWAS to test for causal associations of health risk factors with common diseases.

High loading of polygenic risk in cases with chronic schizophrenia

Genomic risk profile scores (GRPSs) have been shown to predict case–control status of schizophrenia (SCZ), albeit with varying sensitivity and specificity. The extent to which this variability in prediction accuracy is related to differences in sampling strategies is unknown. Danish population-based registers and Neonatal Biobanks were used to identify two independent incident data sets (denoted target and replication) comprising together 1861 cases with SCZ and 1706 controls. A third data set was a German prevalent sample with diagnoses assigned to 1773 SCZ cases and 2161 controls based on clinical interviews. GRPSs were calculated based on the genome-wide association results from the largest SCZ meta-analysis yet conducted. As measures of genetic risk prediction, Nagelkerke pseudo-R2 and variance explained on the liability scale were calculated. GRPS for SCZ showed positive correlations with the number of psychiatric admissions across all P-value thresholds in both the incident and prevalent samples. In permutation-based test, Nagelkerke pseudo-R2 values derived from samples enriched for frequently admitted cases were found to be significantly higher than for the full data sets (Ptarget=0.017, Preplication=0.04). Oversampling of frequently admitted cases further resulted in a higher proportion of variance explained on the liability scale (improvementtarget= 50%; improvementreplication= 162%). GRPSs are significantly correlated with chronicity of SCZ. Oversampling of cases with a high number of admissions significantly increased the amount of variance in liability explained by GRPS. This suggests that at least part of the effect of common single-nucleotide polymorphisms is on the deteriorative course of illness.

Improving genetic prediction by leveraging genetic correlations among human diseases and traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.Genetic prediction of complex traits so far has limited accuracy because of insufficient understanding of the genetic risk. Here, Maier et al. develop an improved method for trait prediction that makes use of genetic correlations between traits and apply it to summary statistics of psychiatric diseases.

Polygenic risk for schizophrenia affects working memory and its neural correlates in healthy subjects

Schizophrenia is a disorder with a high heritability. Patients as well as their first degree relatives display lower levels of performance in a number of cognitive domains compared to subjects without genetic risk. Several studies could link these aberrations to single genetic variants, however, only recently, polygenic risk scores as proxies for genetic risk have been associated with cognitive domains and their neural correlates. In the present study, a sample of healthy subjects (n=137) performed a letter version of the n-back task while scanned with 3-T fMRI. All subjects were genotyped with the PsychChip and polygenic risk scores were calculated based on the PGC2 schizophrenia GWAS results. Polygenic risk for schizophrenia was associated with a lower degree of brain activation in prefrontal areas during the 3-back compared to the 0-back baseline condition. Furthermore, polygenic risk was associated with lower levels of brain activation in the right inferior frontal gyrus during the 3-back compared to a 2-back condition. Polygenic risk leads to a shift in the underlying activation pattern to the left side, thus resembling results reported in patients with schizophrenia. The data may point to polygenic risk for schizophrenia being associated with brain function in a cognitive task known to be impaired in patients and their relatives.

Polygenic risk for depression and the neural correlates of working memory in healthy subjects

INTRODUCTION Major depressive disorder (MDD) patients show impairments of cognitive functioning such as working memory (WM), and furthermore alterations during WM-fMRI tasks especially in frontal and parietal brain regions. The calculation of a polygenic risk score (PRS) can be used to describe the genetic influence on MDD, hence imaging genetic studies aspire to combine both genetics and neuroimaging data to identify the influence of genetic factors on brain functioning. We aimed to detect the effect of MDD-PRS on brain activation during a WM task measured with fMRI and expect healthy individuals with a higher PRS to be more resembling to MDD patients. METHOD In total, n=137 (80 men, 57 women, aged 34.5, SD=10.4years) healthy subjects performed a WM n-back task [0-back (baseline), 2-back and 3-back condition] in a 3T-MRI-tomograph. The sample was genotyped using the Infinium PsychArray BeadChip and a polygenic risk score was calculated for MDD using PGC MDD GWAS results. RESULTS A lower MDD risk score was associated with increased activation in the bilateral middle occipital gyri (MOG), the bilateral middle frontal gyri (MFG) and the right precentral gyrus (PCG) during the 2-back vs. baseline condition. Moreover, a lower PRS was associated with increased brain activation during the 3-back vs. baseline condition in the bilateral cerebellum, the right MFG and the left inferior parietal lobule. A higher polygenic risk score was associated with hyperactivation in brain regions comprising the right MFG and the right supplementary motor area during the 3-back vs. 2-back condition. DISCUSSION The results suggest that part of the WM-related brain activation patterns might be explained by genetic variants captured by the MDD-PRS. Furthermore we were able to detect MDD-associated activation patterns in healthy individuals depending on the MDD-PRS and the task complexity. Additional gene loci could contribute to these task-dependent brain activation patterns.

Rapporteur summaries of plenary, symposia, and oral sessions from the XXIIIrd World Congress of Psychiatric Genetics Meeting in Toronto, Canada, 16-20 October 2015

The XXIIIrd World Congress of Psychiatric Genetics meeting, sponsored by the International Society of Psychiatric Genetics, was held in Toronto, ON, Canada, on 16–20 October 2015. Approximately 700 participants attended to discuss the latest state-of-the-art findings in this rapidly advancing and evolving field. The following report was written by trainee travel awardees. Each was assigned one session as a rapporteur. This manuscript represents the highlights and topics that were covered in the plenary sessions, symposia, and oral sessions during the conference, and contains major notable and new findings.