Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to play an important role in genetic susceptibility to common disease. To address this we undertook a large direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed ~19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated ~50% of all common CNVs larger than 500bp. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell-lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease, IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis, and type 1 diabetes, and TSPAN8 for type 2 diabetes, though in each case the locus had previously been identified in SNP-based studies, reflecting our observation that the majority of common CNVs which are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs which can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.

Recent findings from genetic epidemiology and from genome-wide association studies point strongly to a partial overlap in the genes that contribute susceptibility to schizophrenia and bipolar disorder (BD). Previous data have also directly implicated one of the best supported schizophrenia-associated loci, zinc finger binding protein 804A (ZNF804A), as showing trans-disorder effects, and the same is true for one of the best supported bipolar loci, calcium channel, voltage-dependent, L type, alpha 1C subunit (CACNA1C) which has also been associated with schizophrenia. We have undertaken a cross-phenotype study based upon the remaining variants that show genome-wide evidence for association in large schizophrenia and BD meta-analyses. These comprise in schizophrenia, SNPs in or in the vicinity of transcription factor 4 (TCF4), neurogranin (NRGN) and an extended region covering the MHC locus on chromosome 6. For BD, the strongly supported variants are in the vicinity of ankyrin 3, node of Ranvier (ANK3) and polybromo-1 (PBRM1). Using data sets entirely independent of their original discoveries, we observed strong evidence that the PBRM1 locus is also associated with schizophrenia (P = 0.00015) and nominally significant evidence (P < 0.05) that the NRGN and the extended MHC region are associated with BD. Moreover, considering this highly restricted set of loci as a group, the evidence for trans-disorder effects is compelling (P = 4.7 × 10(-5)). Including earlier reported data for trans-disorder effects for ZNF804A and CACNA1C, six out of eight of the most robustly associated loci for either disorder show trans-disorder effects.

Background & AimsBarrett's esophagus (BE) increases the risk of esophageal adenocarcinoma (EAC). We found the risk to be BE has been associated with single nucleotide polymorphisms (SNPs) on chromosome 6p21 (within the HLA region) and on 16q23, where the closest protein-coding gene is FOXF1. Subsequently, the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON) identified risk loci for BE and esophageal adenocarcinoma near CRTC1 and BARX1, and within 100 kb of FOXP1. We aimed to identify further SNPs that increased BE risk and to validate previously reported associations.MethodsWe performed a genome-wide association study (GWAS) to identify variants associated with BE and further analyzed promising variants identified by BEACON by genotyping 10,158 patients with BE and 21,062 controls.ResultsWe identified 2 SNPs not previously associated with BE: rs3072 (2p24.1; odds ratio [OR] = 1.14; 95% CI: 1.09–1.18; P = 1.8 × 10−11) and rs2701108 (12q24.21; OR = 0.90; 95% CI: 0.86–0.93; P = 7.5 × 10−9). The closest protein-coding genes were respectively GDF7 (rs3072), which encodes a ligand in the bone morphogenetic protein pathway, and TBX5 (rs2701108), which encodes a transcription factor that regulates esophageal and cardiac development. Our data also supported in BE cases 3 risk SNPs identified by BEACON (rs2687201, rs11789015, and rs10423674). Meta-analysis of all data identified another SNP associated with BE and esophageal adenocarcinoma: rs3784262, within ALDH1A2 (OR = 0.90; 95% CI: 0.87–0.93; P = 3.72 × 10−9).ConclusionsWe identified 2 loci associated with risk of BE and provided data to support a further locus. The genes we found to be associated with risk for BE encode transcription factors involved in thoracic, diaphragmatic, and esophageal development or proteins involved in the inflammatory response.

Attention deficit hyperactivity disorder (ADHD) is a common, highly heritable, neurodevelopmental disorder with onset in early childhood. Genes involved in neuronal development and growth are, thus, important etiological candidates and brain-derived neurotrophic factor (BDNF), has been hypothesized to play a role in the pathogenesis of ADHD. BDNF is a member of the neurotrophin family and is involved in the survival and differentiation of dopaminergic neurons in the developing brain (of relevance because drugs that block the dopamine transporter can be effective therapeutically). The common Val66Met functional polymorphism in the human BDNF gene (rs 6265) was genotyped in a collaborative family-based sample of 341 white UK or Irish ADHD probands and their parents. We found evidence for preferential transmission of the valine (G) allele of BDNF (odds ratio, OR ¼ 1.6, P ¼ 0.02) with a strong paternal effect (paternal transmissions: OR ¼ 3.2, P ¼ 0.0005; maternal transmissions: OR ¼ 1.00; P ¼ 1.00). Our findings support the hypothesis that BDNF is involved in the pathogenesis of ADHD. The transmission difference between parents raises the possibility that an epigenetic process may be involved. Keywords: attention deficit hyperactivity disorder; association study; neurotrophic factor; polymorphism Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects 2-5% of school-aged children with more boys diagnosed than girls. 1 It is characterized by marked and pervasive inattention, overactivity and impulsiveness and causes significant social, educational and psychological problems. Quantitative genetic research over the last decade, from family, twin and adoption studies has firmly established that ADHD has a significant genetic contribution. 2 The medications most often used for ADHD are psychostimulants including methylphenidate and dexamphetamine. Precise therapeutic mechanisms of these drugs in ADHD remain unclear, although methylphenidate is known to inhibit the dopamine transporter. As a result, most candidate gene studies to date have focused on the dopamine system. 3 Positive findings have been successfully replicated for variants in the D4 dopamine receptor (DRD4), 4 D5 dopamine receptor (DRD5) 5 and the dopamine transporter (DAT1). 6 These family-based meta-analyses each report small effect sizes with odds ratios of less than 1.5, indicating that if they are true susceptibility variants for ADHD their contribution to the overall phenotype is small. Given that ADHD is a neurodevelopmental disorder, genes involved in neuronal development and growth represent an important set of candidates for involvement in the pathogenesis. One such candidate that has been postulated to play a role in ADHD is BDNF (MIM 113505). 7 The gene encoding BDNF is located at 11p13 and codes for a precursor peptide (proBDNF), which is proteolytically cleaved to form the mature protein. Only one nonsynonymous polymorphism in the human BDNF gene (rs 6265) has been identified, a single nucleotide polymorph...

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.