Schizophrenia risk genes

AbstractPTEN has a strong Mendelian association with autism spectrum disorder (ASD), representing a special case in autism’s complex genetic architecture. Animal modeling for constitutional Pten mutation creates an opportunity to study how disruption of Pten affects neurobiology and glean potential insight into ASD pathogenesis. Subsequently, we comprehensively characterized the neural (phospho)proteome of Ptenm3m4/m3m4 mice, which exhibits cytoplasmic-predominant Pten expression, by applying mass spectrometry technology to their brains at two-weeks- (P14) and six-weeks-of-age (P40). The differentially expressed/phosphorylated proteins were subjected to gene enrichment, pathway, and network analyses to assess the affected biology. We identified numerous differentially expressed/phosphorylated proteins, finding greater dysregulation at P40 consistent with prior transcriptomic data. The affected pathways were largely related to PTEN function or neurological processes, while scant direct overlap was found across datasets. Network analysis pointed to ASD risk genes like Pten and Psd-95 as major regulatory hubs, suggesting they likely contribute to initiation or maintenance of cellular and perhaps organismal phenotypes related to ASD.

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“Guilt by association” is not competitive with genetic association for identifying autism risk genes

Scientific Reports ◽

10.1038/s41598-021-95321-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Margot Gunning ◽

Paul Pavlidis

Keyword(s):

Machine Learning ◽

Genetic Association ◽

Gene Networks ◽

Rare Variants ◽

Association Studies ◽

Genetic Disorders ◽

Autism Spectrum ◽

Biological Data ◽

Disease Genes ◽

Risk Genes

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

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Newly Identified Risk Genes Offer Better Understanding of Sporadic ALS

Neurology Today ◽

10.1097/01.nt.0000295255.92297.91 ◽

2007 ◽

Vol 7 (18) ◽

pp. 15

Author(s):

Jamie Talan

Keyword(s):

Risk Genes ◽

Sporadic Als

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Consensus Recommendations of the German Consortium for Hereditary Breast and Ovarian Cancer

Breast Care ◽

10.1159/000516376 ◽

2021 ◽

pp. 1-9

Author(s):

Kerstin Rhiem ◽

Bernd Auber ◽

Susanne Briest ◽

Nicola Dikow ◽

Nina Ditsch ◽

...

Keyword(s):

Ovarian Cancer ◽

Disease Risk ◽

Interdisciplinary Team ◽

Individual Risk ◽

Patient Counseling ◽

Consensus Process ◽

Breast And Ovarian Cancer ◽

Risk Genes ◽

Therapeutic Measures ◽

Individual Disease

Background: The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) has established a multigene panel (TruRisk®) for the analysis of risk genes for familial breast and ovarian cancer. Summary: An interdisciplinary team of experts from the GC-HBOC has evaluated the available data on risk modification in the presence of pathogenic mutations in these genes based on a structured literature search and through a formal consensus process. Key Messages: The goal of this work is to better assess individual disease risk and, on this basis, to derive clinical recommendations for patient counseling and care at the centers of the GC-HBOC from the initial consultation prior to genetic testing to the use of individual risk-adapted preventive/therapeutic measures.

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Voltage-gated calcium channel blockers for psychiatric disorders: genomic reappraisal

The British Journal of Psychiatry ◽

10.1192/bjp.2019.157 ◽

2019 ◽

Vol 216 (5) ◽

pp. 250-253 ◽

Cited By ~ 8

Author(s):

Paul J. Harrison ◽

Elizabeth M. Tunbridge ◽

Annette C. Dolphin ◽

Jeremy Hall

Keyword(s):

Calcium Channels ◽

Calcium Channel ◽

Psychiatric Disorders ◽

Calcium Channel Blockers ◽

Channel Blockers ◽

Risk Genes ◽

Voltage Gated ◽

Voltage Gated Calcium Channels ◽

Voltage Gated Calcium Channel ◽

Second Use

SummaryWe reappraise the psychiatric potential of calcium channel blockers (CCBs). First, voltage-gated calcium channels are risk genes for several disorders. Second, use of CCBs is associated with altered psychiatric risks and outcomes. Third, research shows there is an opportunity for brain-selective CCBs, which are better suited to psychiatric indications.

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Rare variant analysis of 4241 pulmonary arterial hypertension cases from an international consortium implicates FBLN2, PDGFD, and rare de novo variants in PAH

Genome Medicine ◽

10.1186/s13073-021-00891-1 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Na Zhu ◽

◽

Emilia M. Swietlik ◽

Carrie L. Welch ◽

Michael W. Pauciulo ◽

...

Keyword(s):

Pulmonary Arterial Hypertension ◽

Candidate Genes ◽

Rare Variant ◽

De Novo ◽

Risk Genes ◽

International Consortium ◽

Rare Variant Analysis ◽

New Genes ◽

Pulmonary Arterial ◽

Variant Analysis

Abstract Background Pulmonary arterial hypertension (PAH) is a lethal vasculopathy characterized by pathogenic remodeling of pulmonary arterioles leading to increased pulmonary pressures, right ventricular hypertrophy, and heart failure. PAH can be associated with other diseases (APAH: connective tissue diseases, congenital heart disease, and others) but often the etiology is idiopathic (IPAH). Mutations in bone morphogenetic protein receptor 2 (BMPR2) are the cause of most heritable cases but the vast majority of other cases are genetically undefined. Methods To identify new risk genes, we utilized an international consortium of 4241 PAH cases with exome or genome sequencing data from the National Biological Sample and Data Repository for PAH, Columbia University Irving Medical Center, and the UK NIHR BioResource – Rare Diseases Study. The strength of this combined cohort is a doubling of the number of IPAH cases compared to either national cohort alone. We identified protein-coding variants and performed rare variant association analyses in unrelated participants of European ancestry, including 1647 IPAH cases and 18,819 controls. We also analyzed de novo variants in 124 pediatric trios enriched for IPAH and APAH-CHD. Results Seven genes with rare deleterious variants were associated with IPAH with false discovery rate smaller than 0.1: three known genes (BMPR2, GDF2, and TBX4), two recently identified candidate genes (SOX17, KDR), and two new candidate genes (fibulin 2, FBLN2; platelet-derived growth factor D, PDGFD). The new genes were identified based solely on rare deleterious missense variants, a variant type that could not be adequately assessed in either cohort alone. The candidate genes exhibit expression patterns in lung and heart similar to that of known PAH risk genes, and most variants occur in conserved protein domains. For pediatric PAH, predicted deleterious de novo variants exhibited a significant burden compared to the background mutation rate (2.45×, p = 2.5e−5). At least eight novel pediatric candidate genes carrying de novo variants have plausible roles in lung/heart development. Conclusions Rare variant analysis of a large international consortium identified two new candidate genes—FBLN2 and PDGFD. The new genes have known functions in vasculogenesis and remodeling. Trio analysis predicted that ~ 15% of pediatric IPAH may be explained by de novo variants.

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T Cells: A Growing Universe of Roles in Neurodegenerative Diseases

The Neuroscientist ◽

10.1177/10738584211024907 ◽

2021 ◽

pp. 107385842110249

Author(s):

Dallin Dressman ◽

Wassim Elyaman

Keyword(s):

T Cells ◽

T Cell ◽

Neurodegenerative Diseases ◽

Human Leukocyte ◽

Autoimmune Response ◽

Antigen Specificity ◽

Risk Genes ◽

Leukocyte Antigen ◽

Histocompatibility Complex ◽

The Central Nervous System

T cells play a central role in homeostasis and host defense against infectious diseases. T cell dysregulation can lead to recognizing self-antigens as foreign antigens, causing a detrimental autoimmune response. T cell involvement in multiple sclerosis (MS), long understood to be an autoimmune-mediated neurodegenerative disease, is well characterized. More recently, a role for T cells has also been identified for the neurodegenerative diseases Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Interestingly, several alleles and variants of human leukocyte antigen (HLA) genes have been classified as AD and PD risk genes. HLA codes for components of major histocompatibility complex (MHC) class I or class II, both of which are expressed by microglia, the innate immune cells of the central nervous system (CNS). Thus, both microglia and T cells may potentially interact in an antigen-dependent or independent fashion to shape the inflammatory cascade occurring in neurodegenerative diseases. Dissecting the antigen specificity of T cells may lead to new options for disease-modifying treatments in neurodegenerative diseases. Here, we review the current understanding of T cells in neurodegenerative diseases. We summarize the subsets of T cells, their phenotype and potential functions in animal models and in human studies of neurodegenerative diseases.

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Genome-wide genetic links between amyotrophic lateral sclerosis and autoimmune diseases

BMC Medicine ◽

10.1186/s12916-021-01903-y ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Chun Yu Li ◽

Tian Mi Yang ◽

Ru Wei Ou ◽

Qian Qian Wei ◽

Hui Fang Shang

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Autoimmune Diseases ◽

Genetic Correlation ◽

Functional Enrichment ◽

Genome Wide Association Studies ◽

Risk Genes ◽

Genome Wide ◽

Shared Risk ◽

Lateral Sclerosis ◽

Shared Genetic

Abstract Background Epidemiological and clinical studies have suggested comorbidity between amyotrophic lateral sclerosis (ALS) and autoimmune disorders. However, little is known about their shared genetic architecture. Methods To examine the relation between ALS and 10 autoimmune diseases, including asthma, celiac disease (CeD), Crohn’s disease (CD), inflammatory bowel disease (IBD), multiple sclerosis (MS), psoriasis, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), and ulcerative colitis (UC), and identify shared risk loci, we first estimated the genetic correlation using summary statistics from genome-wide association studies, and then analyzed the genetic enrichment leveraging the conditional false discovery rate statistical method. Results We identified a significant positive genetic correlation between ALS and CeD, MS, RA, and SLE, as well as a significant negative genetic correlation between ALS and IBD, UC, and CD. Robust genetic enrichment was observed between ALS and CeD and MS, and moderate enrichment was found between ALS and UC and T1D. Thirteen shared genetic loci were identified, among which five were suggestively significant in another ALS GWAS, namely rs3828599 (GPX3), rs3849943 (C9orf72), rs7154847 (G2E3), rs6571361 (SCFD1), and rs9903355 (GGNBP2). By integrating cis-expression quantitative trait loci analyses in Braineac and GTEx, we further identified GGNBP2, ATXN3, and SLC9A8 as novel ALS risk genes. Functional enrichment analysis indicated that the shared risk genes were involved in four pathways including membrane trafficking, vesicle-mediated transport, ER to Golgi anterograde transport, and transport to the Golgi and subsequent modification. Conclusions Our findings demonstrate a specific genetic correlation between ALS and autoimmune diseases and identify shared risk loci, including three novel ALS risk genes. These results provide a better understanding for the pleiotropy of ALS and have implications for future therapeutic trials.

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Discovery of breast cancer risk genes and establishment of a prediction model based on estrogen metabolism regulation

BMC Cancer ◽

10.1186/s12885-021-07896-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Feng Zhao ◽

Zhixiang Hao ◽

Yanan Zhong ◽

Yinxue Xu ◽

Meng Guo ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Healthy Subjects ◽

Metabolic Enzyme ◽

Estrogen Metabolism ◽

Polygenic Risk Score ◽

Risk Genes ◽

Polygenic Risk ◽

Enzyme Gene

Abstract Background Multiple common variants identified by genome-wide association studies have shown limited evidence of the risk of breast cancer in Chinese individuals. In this study, we aimed to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes in breast cancer (BC) and establish a risk prediction model composed of estrogen-metabolizing enzyme genes and GWAS-identified breast cancer-related genes based on a polygenic risk score. Methods Unrelated BC patients and healthy subjects were recruited for analysis of estrogen levels and single nucleotide polymorphisms (SNPs) in genes encoding estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes, which was calculated using a Bayesian approach. An independent sample t-test was used to evaluate the differences between PRS scores of BC and healthy subjects. The discriminatory accuracy of the models was compared using the area under the receiver operating characteristic (ROC) curve. Results The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulating in the serum of BC patients. We then established a PRS model to evaluate the role of SNPs in multiple genes. PRS model 1 (M1) was established from SNPs in 6 GWAS-identified high risk genes. On the basis of M1, we added SNPs from 7 estrogen metabolism enzyme genes to establish PRS model 2 (M2). The independent sample t-test results showed that there was no difference between BC and healthy subjects in M1 (P = 0.17); however, there was a significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results showed that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than that of M1 (AUC = 54.56%). Conclusion Estrogen and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has a good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs.

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