Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Identification and Characterization of Genetic Components in Autism Spectrum Disorders 2020

The Identification of the Genetic Components of Autism Spectrum Disorders 2020 will be a useful resource for laboratory and clinical scientists, translational-based researchers, primary healthcare providers and physicians, psychologists/psychiatrists, neurologists, developmental pediatricians, clinical geneticists, teachers, special educators, and caregivers involved with individuals who have autism spectrum disorders (ASD), with the goal to translate information directly to the clinical, education and home settings. Other professionals, students at all levels, and families who are interested in this important neurodevelopmental disorder will find this textbook of value by obtaining a better awareness of the causes, testing, and understanding of genetic components leading to autism, and research that may open avenues for treatment with new approaches. This textbook includes nine chapters divided into three sections (clinical, genetics, other) written by experts in the field dedicated to genetics research and clinical care, description, and treatment by generating reviews for ASD and related disorders. These chapters include information on discoveries, risk factors, causation, diagnosis, treatment, and phenotyping with characterization of genomic or genetic factors and the environment, as genetics play an important role in up to 90% of individuals with autism via over 800 currently recognized genes

Development of a statistical method for the identification of gene-environment interactions

In order to understand common, complex disease it is necessary to consider not just genetic risks and environmental risks, but also the interplay between them. This thesis aims to develop methodology for the detection of gene-environment interactions specifically; both by looking at the strengths and weaknesses of traditional approaches and through the development and testing of a novel statistical method. Developments in genotyping technology enable researchers to collect large volumes of polymorphisms in human genes, yet very few statistical methods are able to handle the volume, variation and complexity of this data, especially in combination with environmental risk factors. Interactions between genes and the environment are often subject to the curse of dimensionality, with each new variable increasing the potential number of interactions exponentially, leading to low power and a high false positive rate. The Mixed Tree Method (MTM) exploits the differences between environmental and genetic variables, by selecting the most appropriate features from conventional methods (including recursive partitioning, random forests and logistic regression) and combining them with new comparison algorithms which rank the genetic variables by the likelihood that they interact with the environmental variable under study. Results show the MTM to be as effective as the most successful current method for identification of interactions, but maintaining a much lower false positive rate and computational burden. As the number of SNPs in the dataset increases, the success of MTM compared to other methods becomes greater while the comparator approaches exhibit computational problems and rapidly increasing processing times. The MTM is also applied to a colorectal cancer dataset to show its use in a practical setting. The results together suggest that MTM could be a useful strategy for identifying gene environment interactions in future studies into complex disease

The Genetics of Anti-Neutrophil Cytoplasmic Antibody Associated Vasculitis (AAV)

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a multi-systemic autoimmune disorder with evidence of circulating pathogenic ANCA. There are two main antigenic targets: proteinase 3 (PR3) and myeloperoxidase (MPO). Previous genome-wide association studies (GWAS) have provided evidence that PR3-AAV and MPO-AAV are genetically distinct autoimmune syndromes, though only three loci specific to PR3-AAV and one to MPO-AAV have been identified to date. With the European Vasculitis Genetics Consortium, we conducted a larger GWAS, powered to discover additional risk loci in both PR3-AAV and MPO-AAV independently. A meta-analysis of two European cohorts was conducted, comprising 1,610 PR3-AAV cases, 870 MPO-AAV cases and 11,947 controls. For PTPN22 (rs6679677), a further replication cohort, previously genotyped using the Sequenom MassARRAY platform, and comprising 1,122 PR3-AAV and 347 MPO-AAV cases and 1,531 controls was included in the combined analysis. This is the largest genome-wide association study of AAV to date and we have identified a total of 12 AAV susceptibility loci. Previously genome-wide significant loci were confirmed, including HLA class II, SERPINA1, PRTN3 and PTPN22. Seven new genome-wide significant loci were identified: three associated with PR3-AAV (BCL2L11-MIR4435-2HG, EBF3-MGMT, IGHV1-69), two with MPO-AAV (BACH2, ANKRD11-SPG7) and two shared by both (CTLA-4 and DGUOK-TET3). Further analyses based on common variants suggested that a substantial component of the genetic architecture was shared between PR3-AAV and MPO-AAV, similar to that observed between ulcerative colitis and Crohn’s disease. In addition, Mendelian randomisation analysis confirmed that a higher eosinophil count increased the risk of PR3-AAV but not MPO-AAV, and this effect might, in part, be modulated by MIR4435-2HG through prolongation of eosinophil survival. MIR4435-2HG encodes a long non-coding RNA that plays a critical role in the regulation of BCL2L11 transcription (a Bcl2 family member essential for controlling apoptosis) in myeloid cells and hence their lifespan. We have also identified a missense variant in IGHV1-69 (rs11845244) that leads to a loss in neutralising function of antibodies generated against the NEAT2 domain of Staphylococcus aureus. This therefore provides a plausible host genetic factor in determining the susceptibility to infectious disease and as a potential driver of PR3-AAV. Overall, this study provides key novel insights into disease biology for AAV and potential therapeutic targets.Wellcome Trust Clinical PhD Fellowshi

Genomic rearrangements and diseases

Copy number variations (CNVs) are major contributors of genomic imbalances disorders. On the short arm of chromosome 16, CNVs of the distal 220 kb BP2-BP3 region show mirror effect on BMI and head size, and association with autism and schizophrenia, as previously reported for the proximal 600 kb BP4-BP5 deletion and duplication. These two CNVs-prone regions at 16p11.2 are also reciprocally engaged in complex chromatin looping, successfully confirmed by 4C-seq, FISH, Hi-C and concomitant expression changes, and are chromatin interactors of other loci linked to autism and/or mirror phenotypes of BMI and head circumference, for example the 2p15 cytoband. Zebrafish modeling of the BP2-BP3 duplication revealed that the overexpression of the linker for activation of T cells (LAT) induces a reduction in dividing cells in the brain and number of post-mitotic neurons in the anterior forebrain, and of intertectal axonal tracts, resulting in microcephaly, and suggested this gene as major contributor in the BP2-BP3 CNVs neurodevelopmental phenotypes. KCTD13, MVP, and MAPK3, three genes mapping within the BP4-BP5 locus and major driver and modifiers, respectively, of the head circumference phenotype linked to that region, and LAT act in additive manner to increase the severity of the microcephaly phenotype, supporting the presence of genetic interaction, in addition to proximity in 3D nuclear space, between these two loci. Smith-Magenis syndrome (SMS) is a developmental disability/multiple congenital anomaly disorder resulting from deletion at 17p11.2 that includes the RAI1 gene or a nucleotide variant in that gene. We investigated a cohort of 15 individuals with a clinical suspicion of SMS, who showed negative deletion and mutational analysis in RAI1. Potentially deleterious variants were identified in eight of these subjects using WES in KMT2D, ZEB2, MAP2K2, GLDC, CASK, MECP2, KDM5C and POGZ. Analyses of coexpression, biomedical text mining, transcriptome profiling of Rai1-/- mice and chromosome contacts suggest that these genes and RAI1 are part of the same disease network. Our 4C-seq results from 16p11.2 and 17p11.2 studies indicate that chromosomal contacts' maps can be exploited to uncover functionally and clinically related genes. These findings also encourage the integration of the results obtained from various genomic approaches to unravel complex disorders and CNVs. Abstract (French) « Structure du génôme et pathologies» La variation du nombre de copies (en anglais, Copy Number Variation, CNV) est un des contributeurs principaux à la pathogenèse des syndromes génétiques rares, mais aussi des maladies multifactorielles fréquentes. Sur le bras court du chromosome 16, les CNVs de la région distale BP2-BP3 de longueur 220 kb conduisent à un effet miroir entre sous- poids et obésité sévère, et micro- et macrocéphalies, et ils sont aussi associés avec l'autisme et la schizophrénie. Des phénotypes similaires ont été observés précédemment sur la même bande chromosomique (16p11.2) pour des délétions et duplications proximales dans la région BP4-BP5 (600 kb). Ces régions BP2-BP3 et BP4- BP5 présentent des contacts chromatiniens réciproques, confirmés avec succès par différentes techniques (4C-seq, FISH, co-régulation dans l'expression des gènes, et des données Hi-C). Elles décrivent aussi des interactions au niveau de la chromatine avec d'autres loci liés à l'autisme et/ou aux phénotypes miroir de l'IMC (Indice de Masse Corporelle) et de la circonférence de la tête, par exemple avec la bande chromosomique 2p15. La modélisation de la duplication de la région BP2-BP3 dans le poisson-zèbre a révélé que la surexpression du gene LAT (en anglais, Linker for Activation of T-cells) diminue la prolifération des cellules dans le cerveau et des neurones post-mitotiques dans le cerveau antérieur et le nombre des axones entre les tecta optiques, au début du développement embryonaire. Dans les stades suivants du développement, nous observons une microcéphalie des poissons. Tous ces éléments indiquent que ce gène est le contributeur essentiel des phénotypes neuro-développementaux des CNVs de la région BP2-BP3. KCTD13, MVP et MAPK3 sont situés dans la région BP4-BP5 et, sont, respectivement, un gène principal et deux gènes modificateurs des anomalies de la taille de la tête liée à cette région. Ces trois gènes et LAT augmentent ensemble de manière additive la gravité de la microcéphalie, en soutenant la présence d'une interaction, pas seulement dans l'espace 3D nucléaire, mais aussi génétique entre les deux loci. Le syndrome de Smith-Magenis (SMS) se caractérise par un retard mental, des dysmorphies, des troubles du comportement et du sommeil très sévères, dues à une microdélétion dans la bande 17p11.2 du chromosome 17, qui comprend le gène RAI1 ou une mutation de ce gène. Nous avons étudié une cohorte de quinze personnes avec un diagnostic de SMS, mais n'ayant pas de délétion ou mutation du gène RAI1. Par le séquençage de l'exome, des mutations potentiellement délétères ont été identifiées chez huit de ces sujets dans les gènes KMT2D, ZEB2, MAP2K2, GLDC, CASK, MECP2, KDM5C et POGZ. Les analyses de la co-expression des gènes, des données de text mining, du profilage du transcriptome des souris Rai1-/- et des contacts chromatiniens font penser que ces gènes et RAI1 font partie du même « disease network ». Les résultats de 4C-seq obtenus par les études des bandes 16p11.2 et 17p11.2 indiquent que les contacts chromosomiques peuvent être exploitées pour découvrir des gènes liés d'un point de vue fonctionnel et clinique. Ces résultats encouragent également l'intégration des données obtenues à partir de différentes approches génomiques pour démêler des troubles complexes et les larges CNVs

Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics

This dissertation aims to develop new algorithms that leverage sparsity and mutual information across data modalities built upon the independent component analysis (ICA) framework to improve the performance of current ICA-based multimodal fusion approaches. These algorithms are further applied to both simulated data and real neuroimaging and genomic data to examine their performance. The identified neuroimaging and genomic patterns can help better delineate the pathology of mental disorders or brain development. To alleviate the signal-background separation difficulties in infomax-decomposed sources for genomic data, we propose a sparse infomax by enhancing a robust sparsity measure, the Hoyer index. Hoyer index is scale-invariant and well suited for ICA frameworks since the scale of decomposed sources is arbitrary. Simulation results demonstrate that sparse infomax increases the component detection accuracy for situations where the source signal-to-background (SBR) ratio is low, particularly for single nucleotide polymorphism (SNP) data. The proposed sparse infomax is further extended into two data modalities as a sparse parallel ICA for applications to imaging genomics in order to investigate the associations between brain imaging and genomics. Simulation results show that sparse parallel ICA outperforms parallel ICA with improved accuracy for structural magnetic resonance imaging (sMRI)-SNP association detection and component spatial map recovery, as well as with enhanced sparsity for sMRI and SNP components under noisy cases. Applying the proposed sparse parallel ICA to fuse the whole-brain sMRI and whole-genome SNP data of 24985 participants in the UK biobank, we identify three stable and replicable sMRI-SNP pairs. The identified sMRI components highlight frontal, parietal, and temporal regions and associate with multiple cognitive measures (with different association strengths in different age groups for the temporal component). Top SNPs in the identified SNP factor are enriched in inflammatory disease and inflammatory response pathways, which also regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region, and the regulation effects are significantly enriched. Applying the proposed sparse parallel ICA to imaging genomics in attention-deficit/hyperactivity disorder (ADHD), we identify and replicate one SNP component related to gray matter volume (GMV) alterations in superior and middle frontal gyri underlying working memory deficit in adults and adolescents with ADHD. The association is more significant in ADHD families than controls and stronger in adults and older adolescents than younger ones. The identified SNP component highlights SNPs in long non-coding RNAs (lncRNAs) in chromosome 5 and in several protein-coding genes that are involved in ADHD, such as MEF2C, CADM2, and CADPS2. Top SNPs are enriched in human brain neuron cells and regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region. Moreover, to increase the flexibility and robustness in mining multimodal data, we propose aNy-way ICA, which optimizes the entire correlation structure of linked components across any number of modalities via the Gaussian independent vector analysis and simultaneously optimizes independence via separate (parallel) ICAs. Simulation results demonstrate that aNy-way ICA recover sources and loadings, as well as the true covariance patterns with improved accuracy compared to existing multimodal fusion approaches, especially under noisy conditions. Applying the proposed aNy-way ICA to integrate structural MRI, fractal n-back, and emotion identification task functional MRIs collected in the Philadelphia Neurodevelopmental Cohort (PNC), we identify and replicate one linked GMV-threat-2-back component, and the threat and 2-back components are related to intelligence quotient (IQ) score in both discovery and replication samples. Lastly, we extend the proposed aNy-way ICA with a reference constraint to enable prior-guided multimodal fusion. Simulation results show that aNy-way ICA with reference recovers the designed linkages between reference and modalities, cross-modality correlations, as well as loading and component matrices with improved accuracy compared to multi-site canonical correlation analysis with reference (MCCAR)+joint ICA under noisy conditions. Applying aNy-way ICA with reference to supervise structural MRI, fractal n-back, and emotion identification task functional MRIs fusion in PNC with IQ as the reference, we identify and replicate one IQ-related GMV-threat-2-back component, and this component is significantly correlated across modalities in both discovery and replication samples.Ph.D

Machine learning for genetic prediction of schizophrenia

The complexity of schizophrenia raises a formidable challenge. Its diverse genetic architecture, influence from environmental factors from the prenatal period through to adolescence, and the absence of a laboratory-based diagnostic test complicate efforts to "carve nature a its joints". Twinned with attempts to disentangle schizophrenia’s origins are those aiming to predict it. Prediction is essential to precision psychiatry and attempts to improve patient outcomes. Genetic prediction only became feasible relatively recently, following the discovery of robust risk loci in association studies. Polygenic risk scoring (PRS) is a popular method which relies on univariable tests of association and typically assumes additivity within and between loci, but explains only a small fraction of liability to schizophrenia. Machine learning (ML) methods have evolved out of the artificial intelligence and statistics communities which learn predictive patterns from labelled data. They are an enticing option in genetics, as they allow for multivariable predictive modelling, complex predictor relationships including interactions and can learn from datasets where the number of predictors exceeds observations. However,their predictive performance in schizophrenia is largely unknown. The ability of penalised logistic regression, support vector machines, random forests (RFs), gradient boosting machines (GBMs) and neural networks to predict schizophrenia from genetic data was investigated. A review systematically assessed predictive performance and methodology in machine learning on psychiatric disorders, finding poor reporting, widespread inadequate modelling approaches and high risk of bias. Simulations assessed performance in the presence of additive or interaction effects. Flexible ML approaches including RFs and GBMs performed best under interactions, but worse than PRS and sparse linear models for additive effects. Evaluation in real data assessed modelling procedures including calibration and deconfounding. Prediction was maximised when combining genetic and non-genetic factors; no evidence was found to support choosing machine learning approaches over logistic regression or PRS

Identification and Characterization of Genetic Components in Autism Spectrum Disorders 2019

The Identification of the Genetic Components of Autism Spectrum Disorders 2019 will serve as a resource for laboratory and clinical scientists as well as translational-based researchers, primary healthcare providers or physicians, psychologists/psychiatrists, neurologists, developmental pediatricians, clinical geneticists, and other healthcare providers, teachers, caregivers and students involved in autism spectrum disorders (ASD) with the goal to translate information directly to the clinic, education and home setting. Other professionals, students and families might find this textbook of value based on better awareness, causes and understanding of genetic components leading to autism and open avenues for treatment. Genetics play a role with up to 90% of autism, with over 800 currently recognized genes contributing to causes, clinical presentation, treatment, and counseling of family members. This textbook includes 13 chapters divided into three sections (clinical, genetics, other) written by experts in the field dedicated to research and clinical care, description, treatment and generating relevant reviews for ASD and related disorders impacting gene expression, profiling, and pathways. Identification of potential risk factors will be discussed, including obesity, microbiota, malignancy, and the immune system, as well as their direct or indirect contribution to ASD treatment and causation