Francesco Bettella

Genome-wide association analysis identifies 13 new risk loci for schizophrenia

Schizophrenia is an idiopathic mental disorder with a heritable component and a substantial public health impact. We conducted a multi-stage genome-wide association study (GWAS) for schizophrenia beginning with a Swedish national sample (5,001 cases and 6,243 controls) followed by meta-analysis with previous schizophrenia GWAS (8,832 cases and 12,067 controls) and finally by replication of SNPs in 168 genomic regions in independent samples (7,413 cases, 19,762 controls and 581 parent-offspring trios). We identified 22 loci associated at genome-wide significance; 13 of these are new, and 1 was previously implicated in bipolar disorder. Examination of candidate genes at these loci suggests the involvement of neuronal calcium signaling. We estimate that 8,300 independent, mostly common SNPs (95% credible interval of 6,300–10,200 SNPs) contribute to risk for schizophrenia and that these collectively account for at least 32% of the variance in liability. Common genetic variation has an important role in the etiology of schizophrenia, and larger studies will allow more detailed understanding of this disorder.

Genome-wide meta-analysis identifies new susceptibility loci for migraine

Migraine is the most common brain disorder, affecting approximately 14% of the adult population, but its molecular mechanisms are poorly understood. We report the results of a meta-analysis across 29 genome-wide association studies, including a total of 23,285 individuals with migraine (cases) and 95,425 population-matched controls. We identified 12 loci associated with migraine susceptibility (P < 5 × 10−8). Five loci are new: near AJAP1 at 1p36, near TSPAN2 at 1p13, within FHL5 at 6q16, within C7orf10 at 7p14 and near MMP16 at 8q21. Three of these loci were identified in disease subgroup analyses. Brain tissue expression quantitative trait locus analysis suggests potential functional candidate genes at four loci: APOA1BP, TBC1D7, FUT9, STAT6 and ATP5B.

Genetic sharing with cardiovascular disease risk factors and diabetes reveals novel bone mineral density loci

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity.

Using genome-wide complex trait analysis to quantify ‘missing heritability’ in Parkinson's disease

Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinsons disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered.

Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci

Converging evidence implicates immune abnormalities in schizophrenia (SCZ), and recent genome-wide association studies (GWAS) have identified immune-related single-nucleotide polymorphisms (SNPs) associated with SCZ. Using the conditional false discovery rate (FDR) approach, we evaluated pleiotropy in SNPs associated with SCZ (n=21 856) and multiple sclerosis (MS) (n=43 879), an inflammatory, demyelinating disease of the central nervous system. Because SCZ and bipolar disorder (BD) show substantial clinical and genetic overlap, we also investigated pleiotropy between BD (n=16 731) and MS. We found significant genetic overlap between SCZ and MS and identified 21 independent loci associated with SCZ, conditioned on association with MS. This enrichment was driven by the major histocompatibility complex (MHC). Importantly, we detected the involvement of the same human leukocyte antigen (HLA) alleles in both SCZ and MS, but with an opposite directionality of effect of associated HLA alleles (that is, MS risk alleles were associated with decreased SCZ risk). In contrast, we found no genetic overlap between BD and MS. Considered together, our findings demonstrate genetic pleiotropy between SCZ and MS and suggest that the MHC signals may differentiate SCZ from BD susceptibility.

Replication and meta-analysis of common variants identifies a genome-wide significant locus in migraine.

Genetic factors contribute to the aetiology of the prevalent form of migraine without aura (MO) and migraine with typical aura (MTA). Due to the complex inheritance of MO and MTA, the genetic background is still not fully established. In a population‐based genome‐wide association study by Chasman et al. (Nat Genet 2011: 43: 695–698), three common variants were found to confer risk of migraine at a genome‐wide significant level (P < 5 × 10−8). We aimed to evaluate the top association single nucleotide polymorphisms (SNPs) from the discovery set by Chasman et al. in a primarily clinic‐based Danish and Icelandic cohort.

Polygenic Risk for Schizophrenia Associated With Working Memory-related Prefrontal Brain Activation in Patients With Schizophrenia and Healthy Controls

Schizophrenia is a highly heritable and polygenic disease, and identified common genetic variants have shown weak individual effects. Many studies have reported altered working memory (WM)-related brain activation in schizophrenia, preferentially in the frontal lobe. Such differences in brain activations could reflect inherited alterations possibly involved in the disease etiology, or rather secondary disease-related mechanisms. The use of polygenic risk scores (PGRS) based on a large number of risk polymorphisms with small effects is a valuable approach to examine the effect of cumulative genetic risk on brain functioning. This study examined the impact of cumulative genetic risk for schizophrenia on WM-related brain activations, assessed with functional magnetic resonance imaging. For each participant (63 schizophrenia patients and 118 healthy controls), we calculated a PGRS for schizophrenia based on 18 862 single-nucleotide polymorphism in a large multicenter genome-wide association study including 9146 schizophrenia patients and 12 111 controls, performed by the Psychiatric Genomics Consortium. As expected, the PGRS was significantly higher in patients compared with healthy controls. Further, the PGRS was related to differences in frontal lobe brain activation between high and low WM demand. Specifically, even in absence of main effects of diagnosis, increased PGRS was associated with decreased activation difference in the right middle-superior prefrontal cortex (BA 10/11) and the right inferior frontal gyrus (BA 45). This effect was seen in both cases and controls, and was not influenced by sex, age, or task performance. The findings support the notion of dysregulation of frontal lobe functioning as an inherited vulnerability factor in schizophrenia.

Shared common variants in prostate cancer and blood lipids

BACKGROUND Epidemiological and clinical studies suggest comorbidity between prostate cancer (PCA) and cardiovascular disease (CVD) risk factors. However, the relationship between these two phenotypes is still not well understood. Here we sought to identify shared genetic loci between PCA and CVD risk factors. METHODS We applied a genetic epidemiology method based on conjunction false discovery rate (FDR) that combines summary statistics from different genome-wide association studies (GWAS), and allows identification of genetic overlap between two phenotypes. We evaluated summary statistics from large, multi-centre GWA studies of PCA (n=50 000) and CVD risk factors (n=200 000) [triglycerides (TG), low-density lipoprotein (LDL) cholesterol and high-density lipoprotein (HDL) cholesterol, systolic blood pressure, body mass index, waist-hip ratio and type 2 diabetes (T2D)]. Enrichment of single nucleotide polymorphisms (SNPs) associated with PCA and CVD risk factors was assessed with conditional quantile-quantile plots and the Anderson-Darling test. Moreover, we pinpointed shared loci using conjunction FDR. RESULTS We found the strongest enrichment of P-values in PCA was conditional on LDL and conditional on TG. In contrast, we found only weak enrichment conditional on HDL or conditional on the other traits investigated. Conjunction FDR identified altogether 17 loci; 10 loci were associated with PCA and LDL, 3 loci were associated with PCA and TG and additionally 4 loci were associated with PCA, LDL and TG jointly (conjunction FDR <0.01). For T2D, we detected one locus adjacent to HNF1B. CONCLUSIONS We found polygenic overlap between PCA predisposition and blood lipids, in particular LDL and TG, and identified 17 pleiotropic gene loci between PCA and LDL, and PCA and TG, respectively. These findings provide novel pathobiological insights and may have implications for trials using targeting lipid-lowering agents in a prevention or cancer setting.

Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic (“z-score”) of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a “relative enrichment score” for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3.

No evidence for association between bipolar disorder risk gene variants and brain structural phenotypes

BACKGROUND While recent genome-wide association studies have identified several new bipolar disorder (BD) risk variants, structural imaging studies have reported enlarged ventricles and volumetric reductions among the most consistent findings. We investigated whether these genetic risk variants could explain some of the structural brain abnormalities in BD. METHODS In a sample of 517 individuals (N=121 BD cases, 116 SZ cases, 61 other psychosis cases and 219 healthy controls), we tested the potential association between nine SNPs in the genes CACNA1C, ANK3, ODZ4 and SYNE1 and eight brain structural measures found to be altered in BD, and if these were specifically affecting the BD sample. We also assessed the polygenic effect of all these 9 SNPs on the brain phenotypes. RESULTS Our most significant result was an association between the risk allele A in CACNA1C SNP rs4775913 and decreased cerebellar volume (pnom.=0.0075) in the total sample, which did not remain significant after multiple testing correction (pthreshold<0.0064). There was no evidence for diagnostic specificity for this association in the BD group. Further, no polygenic effect of these 9 SNPs was observed. LIMITATIONS Low statistical power might increase our type II error rate. CONCLUSIONS The present findings indicate that these risk SNPs do not explain a large proportion of the structural brain alterations in BD. Thus, these genes which are all related to neuronal functions must be involved in other pathophysiological aspects of BD development.