Expression Analysis of Macrodactyly Identifies Pleiotrophin Upregulation

Macrodactyly is a rare family of congenital disorders characterized by the diffuse enlargement of 1 or more digits. Multiple tissue types within the affected digits are involved, but skeletal patterning and gross morphological features are preserved. Not all tissues are equally involved and there is marked heterogeneity with respect to clinical phenotype. The molecular mechanisms responsible for these growth disturbances offer unique insight into normal limb growth and development, in general. To date, no genes or loci have been implicated in the development of macrodactyly. In this study, we performed the first transcriptional profiling of macrodactyly tissue. We found that pleiotrophin (PTN) was significantly overexpressed across all our macrodactyly samples. The mitogenic functions of PTN correlate closely with the clinical characteristics of macrodactyly. PTN thus represents a promising target for further investigation into the etiology of overgrowth phenotypes

Mutations in KCTD1 Cause Scalp-Ear-Nipple Syndrome

Scalp-ear-nipple (SEN) syndrome is a rare, autosomal-dominant disorder characterized by cutis aplasia of the scalp; minor anomalies of the external ears, digits, and nails; and malformations of the breast. We used linkage analysis and exome sequencing of a multiplex family affected by SEN syndrome to identify potassium-channel tetramerization-domain-containing 1 (KCTD1) mutations that cause SEN syndrome. Evaluation of a total of ten families affected by SEN syndrome revealed KCTD1 missense mutations in each family tested. All of the mutations occurred in a KCTD1 region encoding a highly conserved bric-a-brac, tram track, and broad complex (BTB) domain that is required for transcriptional repressor activity. KCTD1 inhibits the transactivation of the transcription factor AP-2 alpha (TFAP2A) via its BTB domain, and mutations in TFAP2A cause cutis aplasia in individuals with branchiooculofacial syndrome (BOFS), suggesting a potential overlap in the pathogenesis of SEN syndrome and BOFS. the identification of KCTD1 mutations in SEN syndrome reveals a role for this BTB-domain-containing transcriptional repressor during ectodermal development.National Institutes of Health National Human Genome Research InstituteLife Sciences Discovery FundWashington Research FoundationMassachusetts Gen Hosp, Cutaneous Biol Res Ctr, Charlestown, MA 02129 USAUniv Washington, Dept Pediat, Seattle, WA 98195 USAUniv Washington, Dept Genome Sci, Seattle, WA 98195 USAUniv Western Sydney Macarthur, Sch Med, Campbelltown, NSW 2560, AustraliaGenet Learning Disabil Serv, Newcastle, NSW 2298, AustraliaJohns Hopkins Univ, Sch Med, McKusick Nathans Inst Genet Med, Baltimore, MD 21205 USAUniversidade Federal de São Paulo, Dept Morphol & Genet, Clin Genet Ctr, BR-04021001 São Paulo, BrazilPontificia Univ Catolica Parana, Dept Internal Med, BR-1155 Curitiba, Parana, BrazilWestern Gen Hosp, South East Scotland Clin Genet Serv, Edinburgh EH4 2XU, Midlothian, ScotlandUniv Florence, Dept Genet & Mol Med, I-50132 Florence, ItalyHop Necker Enfants Malad, Dept Genet, INSERM, U781, F-75015 Paris, FranceUniv Paris Descartes Sorbonne Paris Cite, Inst Imagine, F-75015 Paris, FranceHop Cote Nacre, CHU Caen, Serv Genet, F-14033 Caen 9, FranceUniv Connecticut, Ctr Hlth, Dept Reconstruct Sci, Farmington, CT 06030 USABoston Childrens Hosp, Dept Plast & Oral Surg, Boston, MA 02115 USATreuman Katz Ctr Pediat Bioeth, Seattle Childrens Res Inst, Seattle, WA 98101 USAUniversidade Federal de São Paulo, Dept Morphol & Genet, Clin Genet Ctr, BR-04021001 São Paulo, BrazilNational Institutes of Health National Human Genome Research Institute: 1U54HG006493National Institutes of Health National Human Genome Research Institute: 1RC2HG005608National Institutes of Health National Human Genome Research Institute: 5RO1HG004316Life Sciences Discovery Fund: 2065508Life Sciences Discovery Fund: 0905001Web of Scienc

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder

Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity

Cognitive functions of individuals with psychiatric disorders differ from that of the general population. Such cognitive differences often manifest early in life as differential school performance and have a strong genetic basis. Here we measured genetic predictors of school performance in 30,982 individuals in English, Danish and mathematics via a genome-wide association study (GWAS) and studied their relationship with risk for six major psychiatric disorders. When decomposing the school performance into math and language-specific performances, we observed phenotypically and genetically a strong negative correlation between math performance and risk for most psychiatric disorders. But language performance correlated positively with risk for certain disorders, especially schizophrenia, which we replicate in an independent sample (n = 4547). We also found that the genetic variants relating to increased risk for schizophrenia and better language performance are overrepresented in individuals involved in creative professions (n = 2953) compared to the general population (n = 164,622). The findings together suggest that language ability, creativity and psychopathology might stem from overlapping genetic roots

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder

Background Attention-deficit/hyperactivity disorder (ADHD) shows substantial heritability and is 2-7 times more common in males than females. We examined two putative genetic mechanisms underlying this sex bias: sex-specific heterogeneity and higher burden of risk in female cases. Methods We analyzed genome-wide autosomal common variants from the Psychiatric Genomics Consortium and iPSYCH Project (20,183 cases, 35,191 controls) and Swedish populationregister data (N=77,905 cases, N=1,874,637 population controls). Results Genetic correlation analyses using two methods suggested near complete sharing of common variant effects across sexes, with rg estimates close to 1. Analyses of population data, however, indicated that females with ADHD may be at especially high risk of certain comorbid developmental conditions (i.e. autism spectrum disorder and congenital malformations), potentially indicating some clinical and etiological heterogeneity. Polygenic risk score (PRS) analysis did not support a higher burden of ADHD common risk variants in female cases (OR=1.02 [0.98-1.06], p=0.28). In contrast, epidemiological sibling analyses revealed that the siblings of females with ADHD are at higher familial risk of ADHD than siblings of affected males (OR=1.14, [95% CI: 1.11-1.18], p=1.5E-15). Conclusions Overall, this study supports a greater familial burden of risk in females with ADHD and some clinical and etiological heterogeneity, based on epidemiological analyses. However, molecular genetic analyses suggest that autosomal common variants largely do not explain the sex bias in ADHD prevalence

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder. Conclusions The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum