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Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome-wide association study (GWAS) have yielded significant and replicated results. We performed a GWAS in 2907 cases with AN from 14 countries (15 sites) and 14 860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery data sets. Seventy-six (72 independent) single nucleotide polymorphisms were taken forward for in silico (two data sets) or de novo (13 data sets) replication genotyping in 2677 independent AN cases and 8629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication data sets comprised 5551 AN cases and 21 080 controls. AN subtype analyses (1606 AN restricting; 1445 AN binge-purge) were performed. No findings reached genome-wide significance. Two intronic variants were suggestively associated: rs9839776 (P=3.01 × 10(-7)) in SOX2OT and rs17030795 (P=5.84 × 10(-6)) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (P=5.76 × 10(-)(6)) between CUL3 and FAM124B and rs1886797 (P=8.05 × 10(-)(6)) near SPATA13. Comparing discovery with replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (P=4 × 10(-6)), strongly suggesting that true findings exist but our sample, the largest yet reported, was underpowered for their detection. The accrual of large genotyped AN case-control samples should be an immediate priority for the field.

This study represents the first large-scale investigation of rare (<1% population frequency) copy number variants (CNVs) in anorexia nervosa (AN). Large, rare CNVs are reported to be causally associated with anthropometric traits, neurodevelopmental disorders, and schizophrenia, yet their role in the genetic basis of AN is unclear. Using genome-wide association study (GWAS) array data from the Anorexia Nervosa Genetics Initiative (ANGI), which included 7414 AN case and 5044 controls, we investigated the association of 67 well-established syndromic CNVs and 178 pleiotropic disease-risk dosage-sensitive CNVs with AN. To identify novel CNV regions (CNVRs) that increase the risk of AN, we conducted genome-wide association studies with a focus on rare CNV-breakpoints (CNV-GWAS). We found no net enrichment of rare CNVs, either deletions or duplications, in AN, and none of the well-established syndromic or pleiotropic CNVs had a significant association with AN status. However, the CNV-GWAS found 21 nominally associated CNVRs that contribute to AN risk, covering protein-coding genes implicated in synaptic function, metabolic/mitochondrial factors, and lipid characteristics, like the CD36 (7q21.11) gene, which transports long-chain fatty acids into cells. CNVRs intersecting genes previously related to neurodevelopmental traits include deletions of NRXN1 intron 5 (2p16.3), IMMP2L (7q31.1), and PTPRD (9p23). Overall, given that our study is well powered to detect the CNV burden level reported for schizophrenia, we can conclude that rare CNVs have a limited role in the etiology of AN, as reported for bipolar disorder. Our nominal associations for the 21 discovered CNVRs are consistent with AN being a metabo-psychiatric trait, as demonstrated by the common genetic architecture of AN, and we provide association results to allow for replication in future research., Competing Interests: Competing interests CMB reports receiving royalties from Pearson Education, Inc. The authors have no conflicts of interest to declare., (© 2024. The Author(s).)

Tandem repeat expansions (TREs) are associated with over 60 monogenic disorders and have recently been implicated in complex disorders such as cancer and autism spectrum disorder. The role of TREs in schizophrenia is now emerging. In this study, we have performed a genome-wide investigation of TREs in schizophrenia. Using genome sequence data from 1154 Swedish schizophrenia cases and 934 ancestry-matched population controls, we have detected genome-wide rare (<0.1% population frequency) TREs that have motifs with a length of 2-20 base pairs. We find that the proportion of individuals carrying rare TREs is significantly higher in the schizophrenia group. There is a significantly higher burden of rare TREs in schizophrenia cases than in controls in genic regions, particularly in postsynaptic genes, in genes overlapping brain expression quantitative trait loci, and in brain-expressed genes that are differentially expressed between schizophrenia cases and controls. We demonstrate that TRE-associated genes are more constrained and primarily impact synaptic and neuronal signaling functions. These results have been replicated in an independent Canadian sample that consisted of 252 schizophrenia cases of European ancestry and 222 ancestry-matched controls. Our results support the involvement of rare TREs in schizophrenia etiology., (© 2022. The Author(s).)

Schizophrenia (SCZ) is highly heterogenous and no subtypes characterizing treatment response or longitudinal course well. Cognitive impairment is a core clinical feature of SCZ and a determinant of poorer outcome. Genetic overlap between SCZ and cognitive traits is complex, with limited studies of comprehensive epidemiological and genomic evidence. To examine the relation between SCZ and three cognitive traits, educational attainment (EDU), premorbid cognitive ability, and intellectual disability (ID), we used two Swedish samples: a national cohort (14,230 SCZ cases and 3,816,264 controls) and a subsample with comprehensive genetic data (4992 cases and 6009 controls). Population-based analyses confirmed worse cognition as a risk factor for SCZ, and the pedigree and SNP-based genetic correlations were comparable. In the genotyped cases, those with high EDU and premorbid cognitive ability tended to have higher polygenetic risk scores (PRS) of EDU and intelligence and fewer rare exonic variants. Finally, by applying an empirical clustering method, we dissected SCZ cases into four replicable subgroups characterized by EDU and ID. In particular, the subgroup with higher EDU in the national cohort had fewer adverse outcomes including long hospitalization and death. In the genotyped subsample, this subgroup had higher PRS of EDU and no excess of rare genetic burdens than controls. In conclusion, we found extensive evidence of a robust relation between cognitive traits and SCZ, underscoring the importance of cognition in dissecting the heterogeneity of SCZ., (© 2022. The Author(s).)

Schizophrenia has a heritability of 60-80% 1 , much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

A better understanding of genetic influences on early white matter development could significantly advance our understanding of neurological and psychiatric conditions characterized by altered integrity of axonal pathways. We conducted a genome-wide association study (GWAS) of diffusion tensor imaging (DTI) phenotypes in 471 neonates. We used a hierarchical functional principal regression model (HFPRM) to perform joint analysis of 44 fiber bundles. HFPRM revealed a latent measure of white matter microstructure that explained approximately 50% of variation in our tractography-based measures and accounted for a large proportion of heritable variation in each individual bundle. An intronic SNP in PSMF1 on chromosome 20 exceeded the conventional GWAS threshold of 5 x 10-8 (p = 4.61 x 10-8). Additional loci nearing genome-wide significance were located near genes with known roles in axon growth and guidance, fasciculation, and myelination., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [r g ], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (r g = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (r g = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (r g = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (r gs = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors., (© 2020 Society for the Study of Addiction.)

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profile curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype associations by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of "copy number profile curves" to describe the CNV profile of an individual, and the "common area under the curve (cAUC) kernel" to model the multi-feature CNV effects. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between- and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank., Competing Interests: No authors have competing interests.

Background: Genetic studies of schizophrenia have implicated numerous risk loci including several copy number variants (CNVs) of large effect and hundreds of loci of small effect. In only a few cases has a specific gene been clearly identified. Rare CNVs affecting a single gene offer a potential avenue to discovering schizophrenia risk genes., Methods: CNVs were generated from exome sequencing of 4913 schizophrenia cases and 6188 control subjects from Sweden. We integrated two CNV calling methods (XHMM and ExomeDepth) to expand our set of single-gene CNVs and leveraged two different approaches for validating these variants (quantitative polymerase chain reaction and NanoString)., Results: We found a significant excess of all rare CNVs (deletions: p = .0004, duplications: p = .0006) and single-gene CNVs (deletions: p = .04, duplications: p = .03) in schizophrenia cases compared with control subjects. An expanded set of CNVs generated from integrating multiple approaches showed a significant burden of deletions in 11 of 21 gene sets previously implicated in schizophrenia and across all genes in those sets (p = .008), although no tests survived correction. We performed an extensive validation of all deletions in the significant set of voltage-gated calcium channels among CNVs called from both exome sequencing and genotyping arrays. In total, 4 exonic, single-gene deletions were validated in schizophrenia cases and none in control subjects (p = .039), of which all were identified by exome sequencing., Conclusions: These results point to the potential contribution of single-gene CNVs to schizophrenia, indicate that the utility of exome sequencing for CNV calling has yet to be maximized, and note that single-gene CNVs should be included in gene-focused studies using other classes of variation., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Despite considerable progress in schizophrenia genetics, most findings have been for large rare structural variants and common variants in well-imputed regions with few genes implicated from exome sequencing. Whole genome sequencing (WGS) can potentially provide a more complete enumeration of etiological genetic variation apart from the exome and regions of high linkage disequilibrium. We analyze high-coverage WGS data from 1162 Swedish schizophrenia cases and 936 ancestry-matched population controls. Our main objective is to evaluate the contribution to schizophrenia etiology from a variety of genetic variants accessible to WGS but not by previous technologies. Our results suggest that ultra-rare structural variants that affect the boundaries of topologically associated domains (TADs) increase risk for schizophrenia. Alterations in TAD boundaries may lead to dysregulation of gene expression. Future mechanistic studies will be needed to determine the precise functional effects of these variants on biology.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Anorexia nervosa (AN) is a serious and heritable psychiatric disorder. To date, studies of copy number variants (CNVs) have been limited and inconclusive because of small sample sizes. We conducted a case-only genome-wide CNV survey in 1983 female AN cases included in the Genetic Consortium for Anorexia Nervosa. Following stringent quality control procedures, we investigated whether pathogenic CNVs in regions previously implicated in psychiatric and neurodevelopmental disorders were present in AN cases. We observed two instances of the well-established pathogenic CNVs in AN cases. In addition, one case had a deletion in the 13q12 region, overlapping with a deletion reported previously in two AN cases. As a secondary aim, we also examined our sample for CNVs over 1 Mbp in size. Out of the 40 instances of such large CNVs that were not implicated previously for AN or neuropsychiatric phenotypes, two of them contained genes with previous neuropsychiatric associations, and only five of them had no associated reports in public CNV databases. Although ours is the largest study of its kind in AN, larger datasets are needed to comprehensively assess the role of CNVs in the etiology of AN.

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (odds ratio (OR) = 1.11, P = 5.7 × 10 -15 ), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 × 10 -6 ). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 × 10 -11 ) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 × 10 -5 ). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by nonallelic homologous recombination.

Copy number variants (CNVs) play an important role in the etiology of many diseases such as cancers and psychiatric disorders. Due to a modest marginal effect size or the rarity of the CNVs, collapsing rare CNVs together and collectively evaluating their effect serves as a key approach to evaluating the collective effect of rare CNVs on disease risk. While a plethora of powerful collapsing methods are available for sequence variants (e.g., SNPs) in association analysis, these methods cannot be directly applied to rare CNVs due to the CNV-specific challenges, i.e., the multi-faceted nature of CNV polymorphisms (e.g., CNVs vary in size, type, dosage, and details of gene disruption), and etiological heterogeneity (e.g., heterogeneous effects of duplications and deletions that occur within a locus or in different loci). Existing CNV collapsing analysis methods (a.k.a. the burden test) tend to have suboptimal performance due to the fact that these methods often ignore heterogeneity and evaluate only the marginal effects of a CNV feature. We introduce CCRET, a random effects test for collapsing rare CNVs when searching for disease associations. CCRET is applicable to variants measured on a multi-categorical scale, collectively modeling the effects of multiple CNV features, and is robust to etiological heterogeneity. Multiple confounders can be simultaneously corrected. To evaluate the performance of CCRET, we conducted extensive simulations and analyzed large-scale schizophrenia datasets. We show that CCRET has powerful and robust performance under multiple types of etiological heterogeneity, and has performance comparable to or better than existing methods when there is no heterogeneity.

Copy-number variants (CNVs) are a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize them. It is conceivable that allele-specific reads from high-throughput sequencing data could be leveraged to both enhance CNV detection and produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. In this paper, we develop an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. To evaluate the performance of AS-GENSENG, we conducted extensive simulations, generated empirical data using existing WGS and WES data sets and validated predicted CNVs using an independent methodology. We conclude that AS-GENSENG not only predicts accurate ASCN calls but also improves the accuracy of total copy number calls, owing to its unique ability to exploit information from both total and allele-specific read counts while accounting for various experimental biases in sequence data. Our novel, user-friendly and computationally efficient method and a complete analytic protocol is freely available at https://sourceforge.net/projects/asgenseng/., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Clozapine is a particularly effective antipsychotic medication but its use is curtailed by the risk of clozapine-induced agranulocytosis/granulocytopenia (CIAG), a severe adverse drug reaction occurring in up to 1% of treated individuals. Identifying genetic risk factors for CIAG could enable safer and more widespread use of clozapine. Here we perform the largest and most comprehensive genetic study of CIAG to date by interrogating 163 cases using genome-wide genotyping and whole-exome sequencing. We find that two loci in the major histocompatibility complex are independently associated with CIAG: a single amino acid in HLA-DQB1 (126Q) (P=4.7 × 10(-14), odds ratio (OR)=0.19, 95% confidence interval (CI)=0.12-0.29) and an amino acid change in the extracellular binding pocket of HLA-B (158T) (P=6.4 × 10(-10), OR=3.3, 95% CI=2.3-4.9). These associations dovetail with the roles of these genes in immunogenetic phenotypes and adverse drug responses for other medications, and provide insight into the pathophysiology of CIAG.

Schizophrenia (SCZ) is a highly heritable neuropsychiatric disorder of complex genetic etiology. Previous genome-wide surveys have revealed a greater burden of large, rare copy number variations (CNVs) in SCZ cases and identified multiple rare recurrent CNVs that increase risk of SCZ although with incomplete penetrance and pleiotropic effects. Identification of additional recurrent CNVs and biological pathways enriched for SCZ CNVs requires greater sample sizes. We conducted a genome-wide survey for CNVs associated with SCZ using a Swedish national sample (4719 cases and 5917 controls). High-confidence CNV calls were generated using genotyping array intensity data, and their effect on risk of SCZ was measured. Our data confirm increased burden of large, rare CNVs in SCZ cases as well as significant associations for recurrent 16p11.2 duplications, 22q11.2 deletions and 3q29 deletions. We report a novel association for 17q12 duplications (odds ratio=4.16, P=0.018), previously associated with autism and mental retardation but not SCZ. Intriguingly, gene set association analyses implicate biological pathways previously associated with SCZ through common variation and exome sequencing (calcium channel signaling and binding partners of the fragile X mental retardation protein). We found significantly increased burden of the largest CNVs (>500 kb) in genes present in the postsynaptic density, in genomic regions implicated via SCZ genome-wide association studies and in gene products localized to mitochondria and cytoplasm. Our findings suggest that multiple lines of genomic inquiry--genome-wide screens for CNVs, common variation and exonic variation--are converging on similar sets of pathways and/or genes.

Although copy number variants (CNVs) are important in genomic medicine, CNVs have not been systematically assessed for many complex traits. Several large rare CNVs increase risk for schizophrenia (SCZ) and autism and often demonstrate pleiotropic effects; however, their frequencies in the general population and other complex traits are unknown. Genotyping large numbers of samples is essential for progress. Large cohorts from many different diseases are being genotyped using exome-focused arrays designed to detect uncommon or rare protein-altering sequence variation. Although these arrays were not designed for CNV detection, the hybridization intensity data generated in each experiment could, in principle, be used for gene-focused CNV analysis. Our goal was to evaluate the extent to which CNVs can be detected using data from one particular exome array (the Illumina Human Exome Bead Chip). We genotyped 9100 Swedish subjects (3962 cases with SCZ and 5138 controls) using both standard genome-wide association study (GWAS) and exome arrays. In comparison with CNVs detected using GWAS arrays, we observed high sensitivity and specificity for detecting genic CNVs 400 kb including known pathogenic CNVs along with replicating the literature finding that cases with SCZ had greater enrichment for genic CNVs. Our data confirm the association of SCZ with 16p11.2 duplications and 22q11.2 deletions, and suggest a novel association with deletions at 11q12.2. Our results suggest the utility of exome-focused arrays in surveying large genic CNVs in very large samples; and thereby open the door for new opportunities such as conducting well-powered CNV assessment and comparisons between different diseases. The use of a single platform also minimizes potential confounding factors that could impact accurate detection.

Alopecia areata (AA) is an autoimmune disease that attacks anagen hair follicles. Gene array in graft-induced C3H/HeJ mice revealed that genes involved in retinoic acid (RA) synthesis were increased, whereas RA degradation genes were decreased in AA compared with sham controls. This was confirmed by immunohistochemistry in biopsies from patients with AA and both mouse and rat AA models. RA levels were also increased in C3H/HeJ mice with AA. C3H/HeJ mice were fed a purified diet containing one of the four levels of dietary vitamin A or an unpurified diet 2 weeks before grafting and disease progression followed. High vitamin A accelerated AA, whereas mice that were not fed vitamin A had more severe disease by the end of the study. More hair follicles were in anagen in mice fed high vitamin A. Both the number and localization of granzyme B-positive cells were altered by vitamin A. IFNγ was also the lowest and IL13 highest in mice fed high vitamin A. Other cytokines were reduced and chemokines increased as the disease progressed, but no additional effects of vitamin A were seen. Combined, these results suggest that vitamin A regulates both the hair cycle and immune response to alter the progression of AA.

Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth-based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth-based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.

Tardive dyskinesia (TD) is a debilitating, unpredictable, and often irreversible side effect resulting from chronic treatment with typical antipsychotic agents such as haloperidol. TD is characterized by repetitive, involuntary, purposeless movements primarily of the orofacial region. In order to investigate genetic susceptibility to TD, we used a validated mouse model for a systems genetics analysis geared toward detecting genetic predictors of TD in human patients. Phenotypic data from 27 inbred strains chronically treated with haloperidol and phenotyped for vacuous chewing movements were subject to a comprehensive genomic analysis involving 426,493 SNPs, 4,047 CNVs, brain gene expression, along with gene network and bioinformatic analysis. Our results identified ~50 genes that we expect to have high prior probabilities for association with haloperidol-induced TD, most of which have never been tested for association with human TD. Among our top candidates were genes regulating the development of brain motor control regions (Zic4 and Nkx6-1), glutamate receptors (Grin1 and Grin2a), and an indirect target of haloperidol (Drd1a) that has not been studied as well as the direct target, Drd2.

Extensive population-based genome-wide association studies have identified an association between the FTO gene and BMI; however, the mechanism of action is still unknown. To determine whether FTO may influence weight regulation through psychological and behavioral factors, seven single-nucleotide polymorphisms (SNPs) of the FTO gene were genotyped in 1,085 individuals with anorexia nervosa (AN) and 677 healthy weight controls from the international Price Foundation Genetic Studies of Eating Disorders. Each SNP was tested in association with eating disorder phenotypes and measures that have previously been associated with eating behavior pathology: trait anxiety, harm-avoidance, novelty seeking, impulsivity, obsessionality, compulsivity, and concern over mistakes. After appropriate correction for multiple comparisons, no significant associations between individual FTO gene SNPs and eating disorder phenotypes or related eating behavior pathology were identified in cases or controls. Thus, this study found no evidence that FTO gene variants associated with weight regulation in the general population are associated with eating disorder phenotypes in AN participants or matched controls., (Copyright © 2011 Wiley-Liss, Inc.)

The success of high resolution genetic mapping of disease predisposition and quantitative trait loci in humans and experimental animals depends on the positions of key crossover events around the gene of interest. In mammals, the majority of recombination occurs at highly delimited 1-2 kb long sites known as recombination hotspots, whose locations and activities are distributed unevenly along the chromosomes and are tightly regulated in a sex specific manner. The factors determining the location of hotspots started to emerge with the finding of PRDM9 as a major hotspot regulator in mammals, however, additional factors modulating hotspot activity and sex specificity are yet to be defined. To address this limitation, we have collected and mapped the locations of 4829 crossover events occurring on mouse chromosome 11 in 5858 meioses of male and female reciprocal F1 hybrids of C57BL/6J and CAST/EiJ mice. This chromosome was chosen for its medium size and high gene density and provided a comparison with our previous analysis of recombination on the longest mouse chromosome 1. Crossovers were mapped to an average resolution of 127 kb, and thirteen hotspots were mapped to <8 kb. Most crossovers occurred in a small number of the most active hotspots. Females had higher recombination rate than males as a consequence of differences in crossover interference and regional variation of sex specific rates along the chromosome. Comparison with chromosome 1 showed that recombination events tend to be positioned in similar fashion along the centromere-telomere axis but independently of the local gene density. It appears that mammalian recombination is regulated on at least three levels, chromosome-wide, regional, and at individual hotspots, and these regulation levels are influenced by sex and genetic background but not by gene content.

Using a specialized ribosome system, previous studies have identified G791 in Escherichia coli 16S rRNA as an invariant and essential residue for ribosome function. To investigate the functional role of G791, we searched for multicopy suppressors that partially restored the protein synthesis ability of mutant ribosomes bearing a G to U substitution at position 791 (U791 ribosomes). Analyses of isolated multicopy suppressors showed that overexpression of initiation factor 1 (IF1) enhanced the protein synthesis ability of U791 ribosomes. In contrast, overexpression of initiation factor 2 (IF2) or IF3 did not enhance the protein synthesis ability of wild-type or U791 ribosomes, and overexpression of IF1 did not affect the function of wild-type or mutant ribosomes bearing nucleotide substitutions in other regions of 16S rRNA. Analyses of sucrose gradient profiles of ribosomes showed that overexpression of IF1 marginally enhanced the subunit association of U791 ribosomes and indicated lower binding affinity of U791 ribosomes to IF1. Our findings suggest the involvement of IF1 in the restoration of the P-site function that was impaired by a nucleotide substitution at residue G791., (© 2010 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.)

The Center for Genome Dynamics Single Nucleotide Polymorphism Database (CGDSNPdb) is an open-source value-added database with more than nine million mouse single nucleotide polymorphisms (SNPs), drawn from multiple sources, with genotypes assigned to multiple inbred strains of laboratory mice. All SNPs are checked for accuracy and annotated for properties specific to the SNP as well as those implied by changes to overlapping protein-coding genes. CGDSNPdb serves as the primary interface to two unique data sets, the 'imputed genotype resource' in which a Hidden Markov Model was used to assess local haplotypes and the most probable base assignment at several million genomic loci in tens of strains of mice, and the Affymetrix Mouse Diversity Genotyping Array, a high density microarray with over 600,000 SNPs and over 900,000 invariant genomic probes. CGDSNPdb is accessible online through either a web-based query tool or a MySQL public login. Database URL: http://cgd.jax.org/cgdsnpdb/

Airway hyper-responsiveness (AHR) is a critical phenotype of human asthma and animal models of asthma. Other studies have measured AHR in nine mouse strains, but only six strains have been used to identify genetic loci underlying AHR. Our goals were to increase the genetic diversity of available strains by surveying 27 additional strains, to apply haplotype association mapping to the 36-strain survey, and to identify new genetic determinants for AHR. We derived AHR from the increase in airway resistance in females subjected to increasing levels of methacholine concentrations. We used haplotype association mapping to identify associations between AHR and haplotypes on chromosomes 3, 5, 8, 12, 13, and 14. And we used bioinformatics techniques to narrow the identified region on chromosome 13, reducing the region to 29 candidate genes, with 11 of considerable interest. Our combined use of haplotype association mapping with bioinformatics tools is the first study of its kind for AHR on these 36 strains of mice. Our analyses have narrowed the possible QTL genes and will facilitate the discovery of novel genes that regulate AHR in mice.

Background: Otitis media (OM) is a common worldwide pediatric health care problem that is known to be influenced by genetics. The objective of our study was to use linkage analysis to map possible OM susceptibility genes., Methods: Using a stringent diagnostic model in which only those who underwent tympanostomy tube insertion at least once for recurrent/persistent OM are considered affected, we have carried out a genome-wide linkage scan using the 10K Affymetrix SNP panel. We genotyped 403 Caucasian families containing 1,431 genotyped individuals and 377 genotyped affected sib pairs, and 26 African American families containing 75 genotyped individuals and 27 genotyped affected sib pairs. After careful quality control, non-parametric linkage analysis was carried out using 8,802 SNPs., Results: In the Caucasian-only data set, our most significant linkage peak is on chromosome 17q12 at rs226088 with a p-value of 0.00007. Other peaks of potential interest are on 10q22.3 (0.00181 at rs1878001), 7q33 (0.00105 at rs958408), 6p25.1 (0.00261 at rs554653), and 4p15.2 (0.00301 at rs2133507). In the combined Caucasian and African American dataset, the 10q22.3 peak becomes more significant, with a minimal p-value of 0.00026 at rs719871. Family-based association testing reveals signals near previously implicated genes: 513 kb from SFTPA2 (10q22.3), 48 kb from IFNG (12q14), and 870 kb from TNF (6p21.3)., Conclusion: Our scan does not provide evidence for linkage in the previously reported regions of 10q26.3 and 19q13.43. Our best-supported linkage regions may contain susceptibility genes that influence the risk for recurrent/persistent OM. Plausible candidates in 17q12 include AP2B1, CCL5, and a cluster of other CCL genes, and in 10q22.3, SFTPA2.

Among mammals, genetic recombination occurs at highly delimited sites known as recombination hotspots. They are typically 1-2 kb long and vary as much as a 1,000-fold or more in recombination activity. Although much is known about the molecular details of the recombination process itself, the factors determining the location and relative activity of hotspots are poorly understood. To further our understanding, we have collected and mapped the locations of 5,472 crossover events along mouse Chromosome 1 arising in 6,028 meioses of male and female reciprocal F1 hybrids of C57BL/6J and CAST/EiJ mice. Crossovers were mapped to a minimum resolution of 225 kb, and those in the telomere-proximal 24.7 Mb were further mapped to resolve individual hotspots. Recombination rates were evolutionarily conserved on a regional scale, but not at the local level. There was a clear negative-exponential relationship between the relative activity and abundance of hotspot activity classes, such that a small number of the most active hotspots account for the majority of recombination. Females had 1.2x higher overall recombination than males did, although the sex ratio showed considerable regional variation. Locally, entirely sex-specific hotspots were rare. The initiation of recombination at the most active hotspot was regulated independently on the two parental chromatids, and analysis of reciprocal crosses indicated that parental imprinting has subtle effects on recombination rates. It appears that the regulation of mammalian recombination is a complex, dynamic process involving multiple factors reflecting species, sex, individual variation within species, and the properties of individual hotspots., Competing Interests: The authors have declared that no competing interests exist.

We have created a high-density SNP resource encompassing 7.87 million polymorphic loci across 49 inbred mouse strains of the laboratory mouse by combining data available from public databases and training a hidden Markov model to impute missing genotypes in the combined data. The strong linkage disequilibrium found in dense sets of SNP markers in the laboratory mouse provides the basis for accurate imputation. Using genotypes from eight independent SNP resources, we empirically validated the quality of the imputed genotypes and demonstrated that they are highly reliable for most inbred strains. The imputed SNP resource will be useful for studies of natural variation and complex traits. It will facilitate association study designs by providing high-density SNP genotypes for large numbers of mouse strains. We anticipate that this resource will continue to evolve as new genotype data become available for laboratory mouse strains. The data are available for bulk download or query at http://cgd.jax.org /.

In many organisms, recombination rates differ between the two sexes. Here we show that in mice, this is because of a shorter genomic interference distance in females than in males, measured in Mb. However, the interference distance is the same in terms of bivalent length. We propose a model in which the interference distance in the two sexes reflects the compaction of chromosomes at the pachytene stage of meiosis.

Tub is a member of a small gene family, the tubby-like proteins (TULPs), with predominant expression in neurons. Mice carrying a mutation in Tub develop retinal and cochlear degeneration as well as late-onset obesity with insulin resistance. During behavioral and metabolic testing, we found that homozygous C57BL/6J-Tub(tub) mice have a lower respiratory quotient than C57BL/6J controls before the onset of obesity, indicating that tubby homozygotes fail to activate carbohydrate metabolism and instead rely on fat metabolism for energy needs. In concordance with this, tubby mice show higher excretion of ketone bodies and accumulation of glycogen in the liver. Quantitation of liver mRNA levels shows that, during the transition from light to dark period, tubby mice fail to induce glucose-6-phosphate dehydrogenase (G6pdh), the rate-limiting enzyme in the pentose phosphate pathway that normally supplies NADPH for de novo fatty acid synthesis and glutathione reduction. Reduced G6PDH protein levels and enzymatic activity in tubby mice lead accordingly to lower levels of NADPH and reduced glutathione (GSH), respectively. mRNA levels for the lipolytic enzymes acetyl-CoA synthetase and carnitine palmitoyltransferase are increased during the dark cycle and decreased during the light period, and several citric acid cycle genes are dysregulated in tubby mice. Examination of hypothalamic gene expression showed high levels of preproorexin mRNA leading to accumulation of orexin peptide in the lateral hypothalamus. We hypothesize that abnormal hypothalamic orexin expression leads to changes in liver carbohydrate metabolism and may contribute to the moderate obesity observed in tubby mice.

The term "extreme discordant and concordant" (EDAC) sampling has been used to describe a variety of strategies for quantitative trait locus mapping using sibling pairs sampled from the corners of the bivariate trait distribution. The principle of the design is to gain efficiency by genotyping only the most informative of the available sibling pairs. EDAC-type designs have been studied in a number of papers, and have been applied in a few others. This literature is somewhat out of date, however, because there are many new statistics that are appropriate for EDAC data. With newer statistics, the power of EDAC designs can be improved. Moreover, the relative power of different designs must be re-evaluated, because the newer statistics improve the power of some designs more than others. That is, there is a circular relationship between design and statistic choices. In this report, we review a number of available design and statistic choices for EDAC studies, and use simulation to show what statistics are most powerful for each design. We then use those more powerful statistics to suggest strategies for making design choices among various EDAC and non-EDAC designs that use sibling pairs. We find that when genotyping must be minimized, an EDAC design with predominantly discordant pairs is the best choice, and when a balance of genotyping and phenotyping effort must be achieved, single proband ascertainment can do better. We also show that moderately selected samples (as opposed to very extreme samples) can be an efficient choice for many studies., ((c) 2005 Wiley-Liss, Inc.)

Previously, Szatkiewicz and colleagues evaluated the performance of a wide variety of statistics for quantitative-trait-locus linkage, using discordant sibling pairs. They found that the most powerful statistics, in general, were a score statistic and a "composite statistic." However, whereas these two statistics have equal power under ideal conditions, each has limitations that reduce its power in certain circumstances. The score statistic depends on estimates of trait parameters and can lose a lot of power if those estimates are incorrect. The composite statistic is not sensitive to trait-parameter estimates but does depend on arbitrary weights that must be chosen on the basis of the ascertainment scheme. In this report, we elucidate the algebraic relationship between the score and composite statistics and then use that relationship to suggest a new statistic that combines the best properties of both. We call our new statistic the "robust discordant pair" (RDP) statistic. We report simulation studies to show that the RDP statistic does, indeed, have all of the strengths and none of the weaknesses of the score and composite statistics.

Extreme discordant sibling pairs (EDSPs) are theoretically powerful for the mapping of quantitative-trait loci (QTLs) in humans. EDSPs have not been used much in practice, however, because of the need to screen very large populations to find enough pairs that are extreme and discordant. Given appropriate statistical methods, another alternative is to use moderately discordant sibling pairs (MDSPs)--pairs that are discordant but not at the far extremes of the distribution. Such pairs can be powerful yet far easier to collect than extreme discordant pairs. Recent work on statistical methods for QTL mapping in humans has included a number of methods that, though not developed specifically for discordant pairs, may well be powerful for MDSPs and possibly even EDSPs. In the present article, we survey the new statistics and discuss their applicability to discordant pairs. We then use simulation to study the type I error and the power of various statistics for EDSPs and for MDSPs. We conclude that the best statistic(s) for discordant pairs (moderate or extreme) is (are) to be found among the new statistics. We suggest that the new statistics are appropriate for many other designs as well-and that, in fact, they open the way for the exploration of entirely novel designs.

During the past few years, there has been a great deal of new work on methods for mapping quantitative-trait loci by use of sibling pairs and sibships. There are several new methods based on linear regression, as well as several more that are based on score statistics. In theory, most of the new methods should be relatively robust to violations of distributional assumptions and to selected sampling, but, in practice, there has been little evaluation of how the methods perform on selected samples. We survey most of the new regression-based statistics and score statistics and propose a few minor variations on the score statistics. We use simulation to evaluate the type I error and the power of all of the statistics, considering (a) population samples of sibling pairs and (b) sibling pairs ascertained on the basis of at least one sibling with a trait value in the top 10% of the distribution. Most of the statistics have correct type I error for selected samples. The statistics proposed by Xu et al. and by Sham and Purcell are generally the most powerful, along with one of our score statistic variants. Even among the methods that are most powerful for "nice" data, some are more robust than others to non-Gaussian trait models and/or misspecified trait parameters.