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

Preferential loss of mismatch repair function in refractory and relapsed acute myeloid leukemia: potential contribution to AML progression

Acute myeloid leukemia (AML) is an aggressive hematological cancer. Despite therapeutic regimens that lead to complete remission, the vast majority of patients undergo relapse. The molecular mechanisms underlying AML development and relapse remain incompletely defined. To explore whether loss of DNA mismatch repair (MMR) function is involved in AML, we screened two key MMR genes, MSH2 and MLH1, for mutations and promoter hypermethylation in leukemia specimens from 53 AML patients and blood from 17 non-cancer controls. We show here that whereas. no amino acid alteration or promoter hypermethylation was detected in all control samples, 18 AML patients exhibited either mutations in MMR genes or hypermethylation in the MLH1 promoter. In vitro functional MMR analysis revealed that almost all the mutations analyzed resulted in loss of MMR function. MMR defects were significantly more frequent in patients with refractory or relapsed AML compared with newly diagnosed patients. These observations suggest for the first time that the loss of MMR function is associated with refractory and relapsed AML and may contribute to disease pathogenesis

Genome-Wide DNA Methylation Analysis of Systemic Lupus Erythematosus Reveals Persistent Hypomethylation of Interferon Genes and Compositional Changes to CD4+ T-cell Populations

<div><p>Systemic lupus erythematosus (SLE) is an autoimmune disease with known genetic, epigenetic, and environmental risk factors. To assess the role of DNA methylation in SLE, we collected CD4+ T-cells, CD19+ B-cells, and CD14+ monocytes from 49 SLE patients and 58 controls, and performed genome-wide DNA methylation analysis with Illumina Methylation450 microarrays. We identified 166 CpGs in B-cells, 97 CpGs in monocytes, and 1,033 CpGs in T-cells with highly significant changes in DNA methylation levels (p<1×10⁻⁸) among SLE patients. Common to all three cell-types were widespread and severe hypomethylation events near genes involved in interferon signaling (type I). These interferon-related changes were apparent in patients collected during active and quiescent stages of the disease, suggesting that epigenetically-mediated hypersensitivity to interferon persists beyond acute stages of the disease and is independent of circulating interferon levels. This interferon hypersensitivity was apparent in memory, naïve and regulatory T-cells, suggesting that this epigenetic state in lupus patients is established in progenitor cell populations. We also identified a widespread, but lower amplitude shift in methylation in CD4+ T-cells (>16,000 CpGs at FDR<1%) near genes involved in cell division and MAPK signaling. These cell type-specific effects are consistent with disease-specific changes in the composition of the CD4+ population and suggest that shifts in the proportion of CD4+ subtypes can be monitored at CpGs with subtype-specific DNA methylation patterns.</p></div

Estimation of cell-type composition including T and B cell subtypes for whole blood methylation microarray data

DNA methylation levels vary markedly by cell-type makeup of a sample. Understanding these differences and estimating the cell-type makeup of a sample is an important aspect of studying DNA methylation. DNA from leukocytes in whole blood is simple to obtain and pervasive in research. However, leukocytes contain many distinct cell types and subtypes. We propose a two-stage model that estimates the proportions of 6 main cell types in whole blood (CD4+ T cells, CD8+ T cells, monocytes, B cells, granulocytes, and natural killer cells) as well as subtypes of T and B cells. Unlike previous methods that only estimate overall proportions of CD4+ T cell, CD8+ T cells, and B cells, our model is able to estimate proportions of naïve, memory, and regulatory CD4+ T cells as well as naïve and memory CD8+ T cells and naïve and memory B cells. Using real and simulated data, we are able to demonstrate that our model is able to reliably estimate proportions of these cell types and subtypes. In studies with DNA methylation data from Illumina’s HumanMethylation450k arrays, our estimates will be useful both for testing for associations of cell type and subtype composition with phenotypes of interest as well as for adjustment purposes to prevent confounding in epigenetic association studies. Additionally, our method can be easily adapted for use with whole genome bisulfite sequencing data or any other genome-wide methylation data platform

Common and cell type-specific DNA methylation changes in SLE.

<p>Differences in mean methylation between SLE and controls are plotted for each cell type at each probe near two genes. <b>A.</b> The IRF7 gene shows hypomethylation across all three cell-types at a CpG island, plus monocyte-specific hypomethylation further into the gene body. <b>B.</b> The IKZF4 gene shows T-cell-specific hypomethylation at the 5′ end of the gene. Red dots indicate p<1×10⁻⁸. Yellow dots indicate FDR<1%.</p

Comparison of the SLE-control methylation differences in sorted T-cell populations.

<p>Each scatter plot represents 1,031 CpGs that had p<1×10⁻⁸ in CD4+ T-cells in our SLE-control association tests. The Y-axis for all plots is the mean SLE-control methylation delta at these CpGs in the initial cohort. The X-axis for each plot is the mean SLE-control methylation delta at the same CpGs in our validation cohort, using (<b>A</b>) total CD4+, (<b>B</b>) CD4+Memory, (<b>C</b>) CD4+Naïve, or (<b>D</b>) CD4+Regulatory cells. The red dots represent CpGs near IFN-regulated genes and the squared correlation coefficients (R²) represent the values for all plotted CpGs (upper left) or IFN CpGs only (lower right).</p

Disease activity QQ-Plots and the persistence of hypomethylation in quiescent patients.

<p><b>A.</b> QQ-Plots of the p-values from the flare versus quiescent association analysis for each cell type, illustrating the lack of activity-dependent DNA methylation. <b>B.</b> Boxplots of the methylation difference between each individual and the mean of all controls at CpGs in IFN-regulated genes among those that were highly significant in the SLE-control tests. The groups are labeled C, Control, F, SLE collected during a flare, and Q, SLE collected during quiescence.</p

Functional analysis of significant CpGs in three cell types.

<p><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003678#s2" target="_blank">Results</a> from DAVID/Panther GO term analysis for the highly significant CpGs in each cell type and the mildly significant CpGs in T-cells.</p

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.c.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders

Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n similar to 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders