Search

Associations of copy number variations (CNVs) with complex traits are challenging to study because of their low frequency. Here, the authors analyse SNP array and array comparative genomic hybridization data of 100,028 individuals and report their associations with immune-related, cardiometabolic and neuropsychiatric diseases as well as cancer.

Stimulant drugs are most commonly used to treat ADHD. Here, the authors demonstrate that in adolescents with ADHD who also have genetic variation in genes impacting metabotropic glutamate signaling, the non-stimulant mGluR activator fasoracetam is well tolerated and may be beneficial in alleviating symptoms of this disease.

A wide range of clinical findings have been associated with mutations in Syntaxin Binding Protein 1 (STXBP1), including multiple forms of epilepsy, nonsyndromic intellectual disability, and movement disorders. STXBP1 mutations have recently been associated with mitochondrial pathology, although it remains unclear if this phenotype is a part of the core feature for this gene disorder. We report a 7-year-old boy who presented for diagnostic evaluation of intractable epilepsy, episodic ataxia, resting tremor, and speech regression following a period of apparently normal early development. Mild lactic acidemia was detected on one occasion at the time of an intercurrent illness. Due to the concern for mitochondrial disease, ophthalmologic evaluation was performed that revealed bilateral midperiphery pigmentary mottling. Optical coherence tomography (OCT) testing demonstrated a bilaterally thickened ganglion cell layer in the perifovea. Skeletal muscle biopsy analysis showed no mitochondrial abnormalities or respiratory chain dysfunction. Exome sequencing identified a de novo c.1651C>T (p.R551C) mutation in STXBP1. Although mitochondrial dysfunction has been reported in some individuals, our proband had only mild lactic acidemia and no skeletal muscle tissue evidence of mitochondrial disease pathology. Thus, mitochondrial dysfunction is not an obligate feature of STXBP1 disease.

Lung function is a heritable trait and serves as an important clinical predictor of morbidity and mortality for pulmonary conditions in adults, however, despite its importance, no studies have focused on uncovering pediatric-specific loci influencing lung function. To identify novel genetic determinants of pediatric lung function, we conducted a genome-wide association study (GWAS) of four pulmonary function traits, including FVC, FEV1, FEV1/FVC and FEF25-75% in 1556 children. Further, we carried out gene network analyses for each trait including all SNPs with a P-value of

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of approximately 0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.