Common genetic risk factors in ASD and ADHD co-occurring families.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two major neurodevelopmental disorders that frequently co-occur. However, the genetic mechanism of the co-occurrence remains unclear. The New Jersey Language and Autism Genetics Study (NJLAGS) collected more than 100 families with at least one member affected by ASD. NJLAGS families show a high prevalence of ADHD and provide a good opportunity to study shared genetic risk factors for ASD and ADHD. The linkage study of the NJLAGS families revealed regions on chromosomes 12 and 17 that are significantly associated with ADHD. Using whole-genome sequencing data on 272 samples from 73 NJLAGS families, we identified potential risk genes for ASD and ADHD. Within the linkage regions, we identified 36 genes that are associated with ADHD using a pedigree-based gene prioritization approach. KDM6B (Lysine Demethylase 6B) is the highest-ranking gene, which is a known risk gene for neurodevelopmental disorders, including ASD and ADHD. At the whole-genome level, we identified 207 candidate genes from the analysis of both small variants and structure variants, including both known and novel genes. Using enrichment and protein-protein interaction network analyses, we identified gene ontology terms and pathways enriched for ASD and ADHD candidate genes, such as cilia function and cation channel activity. Candidate genes and pathways identified in our study improve the understanding of the genetic etiology of ASD and ADHD and will lead to new diagnostic or therapeutic interventions for ASD and ADHD in the future.

Structural Variations Contribute to the Genetic Etiology of Autism Spectrum Disorder and Language Impairments.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by restrictive interests and/or repetitive behaviors and deficits in social interaction and communication. ASD is a multifactorial disease with a complex polygenic genetic architecture. Its genetic contributing factors are not yet fully understood, especially large structural variations (SVs). In this study, we aimed to assess the contribution of SVs, including copy number variants (CNVs), insertions, deletions, duplications, and mobile element insertions, to ASD and related language impairments in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Within the cohort, ~77% of the families contain SVs that followed expected segregation or de novo patterns and passed our filtering criteria. These SVs affected 344 brain-expressed genes and can potentially contribute to the genetic etiology of the disorders. Gene Ontology and protein-protein interaction network analysis suggested several clusters of genes in different functional categories, such as neuronal development and histone modification machinery. Genes and biological processes identified in this study contribute to the understanding of ASD and related neurodevelopment disorders.

The value of regenotyping older linkage data sets with denser marker panels.

OBJECTIVES: Linkage analysis can help determine regions of interest in whole-genome sequence studies. However, many linkage studies rely on older microsatellite (MSAT) panels. We set out to determine whether results would change if we regenotyped families using a dense map of SNPs. METHODS: We selected 47 Hispanic-American families from the NIMH Repository and Genomics Resource (NRGR) schizophrenia data repository. We regenotyped all individuals with DNA available from the NRGR on the Affymetrix Lat Array. After optimizing SNP selection for inclusion on the linkage map, we compared information content (IC) and linkage results using MSAT, SNP and MSAT+SNP maps. RESULTS: As expected, SNP provided a higher average IC (0.78, SD 0.03) than MSAT (0.51, SD 0.10) in a direct 'apples-to-apples' comparison using only individuals genotyped on both platforms; while MSAT+SNP provided only a slightly higher IC (0.82, SD 0.03). However, when utilizing all available individuals, including those who had genotypes available on only one platform, the IC was substantially increased using MSAT+SNP (0.76, SD 0.05) compared to SNP (0.61, SD 0.02). Linkage results changed appreciably between MSAT and MSAT+SNP in terms of magnitude, rank ordering and localization of peaks. CONCLUSIONS: Regenotyping older family data can substantially alter the conclusions of linkage analyses.

MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders.

Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder with a complex and heterogeneous genetic etiology. MicroRNA (miRNA), a class of small non-coding RNAs, could regulate ASD risk genes post-transcriptionally and affect broad molecular pathways related to ASD and associated disorders. Using whole-genome sequencing, we analyzed 272 samples in 73 families in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Families with at least one ASD patient were recruited and were further assessed for language impairment, reading impairment, and other associated phenotypes. A total of 5104 miRNA variants and 1,181,148 3' untranslated region (3' UTR) variants were identified in the dataset. After applying several filtering criteria, including population allele frequency, brain expression, miRNA functional regions, and inheritance patterns, we identified high-confidence variants in five brain-expressed miRNAs (targeting 326 genes) and 3' UTR miRNA target regions of 152 genes. Some genes, such as SCP2 and UCGC, were identified in multiple families. Using Gene Ontology overrepresentation analysis and protein-protein interaction network analysis, we identified clusters of genes and pathways that are important for neurodevelopment. The miRNAs and miRNA target genes identified in this study are potentially involved in neurodevelopmental disorders and should be considered for further functional studies.

Increasing genotype-phenotype model determinism: application to bivariate reading/language traits and epistatic interactions in language-impaired families.

While advances in network and pathway analysis have flourished in the era of genome-wide association analysis, understanding the genetic mechanism of individual loci on phenotypes is still readily accomplished using genetic modeling approaches. Here, we demonstrate two novel genotype-phenotype models implemented in a flexible genetic modeling platform. The examples come from analysis of families with specific language impairment (SLI), a failure to develop normal language without explanatory factors such as low IQ or inadequate environment. In previous genome-wide studies, we observed strong evidence for linkage to 13q21 with a reading phenotype in language-impaired families. First, we elucidate the genetic architecture of reading impairment and quantitative language variation in our samples using a bivariate analysis of reading impairment in affected individuals jointly with language quantitative phenotypes in unaffected individuals. This analysis largely recapitulates the baseline analysis using the categorical trait data (posterior probability of linkage (PPL) = 80%), indicating that our reading impairment phenotype captured poor readers who also have low language ability. Second, we performed epistasis analysis using a functional coding variant in the brain-derived neurotrophic factor (BDNF) gene previously associated with reduced performance on working memory tasks. Modeling epistasis doubled the evidence on 13q21 and raised the PPL to 99.9%, indicating that BDNF and 13q21 susceptibility alleles are jointly part of the genetic architecture of SLI. These analyses provide possible mechanistic insights for further cognitive neuroscience studies based on the models developed herein.

Identification of possible genetic alterations in the breast cancer cell line MCF-7 using high-density SNP genotyping microarray.

CONTEXT: Cancer cell lines are used extensively in various research. Knowledge of genetic alterations in these lines is important for understanding mechanisms underlying their biology. However, since paired normal tissues are usually unavailable for comparison, precisely determining genetic alterations in cancer cell lines is difficult. To address this issue, a highly efficient and reliable method is developed. AIMS: Establishing a highly efficient and reliable experimental system for genetic profiling of cell lines. MATERIALS AND METHODS: A widely used breast cancer cell line, MCF-7, was genetically profiled with 4,396 single nucleotide polymorphisms (SNPs) spanning 11 whole chromosomes and two other small regions using a newly developed high-throughput multiplex genotyping approach. RESULTS: The fractions of homozygous SNPs in MCF-7 (13.3%) were significantly lower than those in the control cell line and in 24 normal human individuals (25.1% and 27.4%, respectively). Homozygous SNPs in MCF-7 were found in clusters. The sizes of these clusters were significantly larger than the expected based on random allelic combination. Fourteen such regions were found on chromosomes 1p, 1q, 2q, 6q, 13, 15q, 16q, 17q and 18p in MCF-7 and two in the small regions. CONCLUSIONS: These results are generally concordant with those obtained using different approaches but are better in defining their chromosomal positions. The used approach provides a reliable way to detecting possible genetic alterations in cancer cell lines without paired normal tissues.

A highly sensitive and specific system for large-scale gene expression profiling.

BACKGROUND: Rapid progress in the field of gene expression-based molecular network integration has generated strong demand on enhancing the sensitivity and data accuracy of experimental systems. To meet the need, a high-throughput gene profiling system of high specificity and sensitivity has been developed. RESULTS: By using specially designed primers, the new system amplifies sequences in neighboring exons separated by big introns so that mRNA sequences may be effectively discriminated from other highly related sequences including their genes, unprocessed transcripts, pseudogenes and pseudogene transcripts. Probes used for microarray detection consist of sequences in the two neighboring exons amplified by the primers. In conjunction with a newly developed high-throughput multiplex amplification system and highly simplified experimental procedures, the system can be used to analyze >1,000 mRNA species in a single assay. It may also be used for gene expression profiling of very few (n = 100) or single cells. Highly reproducible results were obtained from duplicate samples with the same number of cells, and from those with a small number (100) and a large number (10,000) of cells. The specificity of the system was demonstrated by comparing results from a breast cancer cell line, MCF-7, and an ovarian cancer cell line, NCI/ADR-RES, and by using genomic DNA as starting material. CONCLUSION: Our approach may greatly facilitate the analysis of combinatorial expression of known genes in many important applications, especially when the amount of RNA is limited.

Lipopolysaccharide (LPS) potentiates hydrogen peroxide toxicity in T98G astrocytoma cells by suppression of anti-oxidative and growth factor gene expression.

BACKGROUND: Lipopolysaccharide (LPS) is a cell wall component of Gram-negative bacteria with proved role in pathogenesis of sepsis. Brain injury was observed with both patients dead from sepsis and animal septic models. However, in vitro administration of LPS has not shown obvious cell damage to astrocytes and other relative cell lines while it does cause endothelial cell death in vitro. These observations make it difficult to understand the role of LPS in brain parenchymal injury. RESULTS: To test the hypothesis that LPS may cause biological changes in astrocytes and make the cells to become vulnerable to reactive oxygen species, a recently developed highly sensitive and highly specific system for large-scale gene expression profiling was used to examine the gene expression profile of a group of 1,135 selected genes in a cell line, T98G, a derivative of human glioblastoma of astrocytic origin. By pre-treating T98G cells with different dose of LPS, it was found that LPS treatment caused a broad alteration in gene expression profile, but did not cause obvious cell death. However, after short exposure to H2O2, cell death was dramatically increased in the LPS pretreated samples. Interestingly, cell death was highly correlated with down-regulated expression of antioxidant genes such as cytochrome b561, glutathione s-transferase a4 and protein kinase C-epsilon. On the other hand, expression of genes encoding growth factors was significantly suppressed. These changes indicate that LPS treatment may suppress the anti-oxidative machinery, decrease the viability of the T98G cells and make the cells more sensitive to H2O2 stress. CONCLUSION: These results provide very meaningful clue for further exploring and understanding the mechanism underlying astrocyte injury in sepsis in vivo, and insight for why LPS could cause astrocyte injury in vivo, but not in vitro. It will also shed light on the therapeutic strategy of sepsis.

Improvements to bead-based oligonucleotide ligation SNP genotyping assays.

We describe a bead-based, multiplexed, oligonucleotide ligation assay (OLA) performed on the Luminex flow cytometer. Differences between this method and those previously reported include the use of far fewer beads and the use of a universal oligonucleotide for signal detection. These innovations serve to significantly reduce the cost of the assay, while maintaining robustness and accuracy. Comparisons are made between the Luminex OLA and both pyrosequencing and direct sequencing. Experiments to assess conversion rates, call rates, and concordance across technical replicates are also presented.

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays.

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of approximately 160,000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300,000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from http://www2.umdnj.edu/lilabweb/publications/AccuTyping.html.

Validation of a microRNA target site polymorphism in H3F3B that is potentially associated with a broad schizophrenia phenotype.

Despite much progress, few genetic findings for schizophrenia have been assessed by functional validation experiments at the molecular level. We previously reported evidence for genetic linkage of broadly defined schizophrenia to chromosome 17q25 in a sample of 24 multiplex families. 2,002 SNPs under this linkage peak were analyzed for evidence of linkage disequilibrium using the posterior probability of linkage (PPL) framework. SNP rs1060120 produced the strongest evidence for association, with a PPLD|L score of 0.21. This SNP is located within the 3'UTR of the histone gene H3F3B and colocalizes with potential gene target miR-616. A custom miRNA target prediction program predicted that the binding of miR-616 to H3F3B transcripts would be altered by the allelic variants of rs1060120. We used dual luciferase assays to experimentally validate this interaction. The rs1060120 A allele significantly reduced luciferase expression, indicating a stronger interaction with miR-616 than the G allele (p = 0.000412). These results provide functional validation that this SNP could alter schizophrenia epigenetic mechanisms thereby contributing to schizophrenia-related disease risk.

Differential affinity and cooperativity functions of the amino-terminal 70 residues of lambda integrase.

The site-specific recombinase (Int) of bacteriophage lambda is a heterobivalent DNA-binding protein that binds two different classes of DNA-binding sites within its recombination target sites. The several functions of Int are apportioned between a large carboxy-terminal domain that cleaves and ligates DNA at each of its four "core-type" DNA-binding sites and a small amino-terminal domain, whose primary function is binding to each of its five "arm-type" DNA sites, which are distant from the core region. Int bridges between the two classes of binding sites are facilitated by accessory DNA-bending proteins that along with Int comprise higher-order recombinogenic complexes. We show here that although the 64 amino-terminal residues of Int bind efficiently to a single arm site, this protein cannot form doubly bound complexes on adjacent arm sites. However, 1-70 Int does show the same cooperative binding to adjacent arm sites as the full length protein. We also found that 1-70 Int specifies cooperative interactions with the accessory protein Xis when the two are bound to their adjacent cognate sites P2 and X1, respectively. To complement the finding that these two amino-terminal domain functions (along with arm DNA binding) are all specified by residues 1-70, we determined that Thr75 is the first residue of the minimal carboxy-terminal domain, thereby identifying a specific interdomain linker region. We have measured the affinity constants for Int binding to each of the five arm sites and the cooperativity factors for Int binding to the two pairs of adjacent arm sites, and we have identified several DNA structural features that contribute to the observed patterns of Int binding to arm sites. Taken together, the results highlight several interesting features of arm DNA binding that invite speculation about additional levels of complexity in the regulation of lambda site-specific recombination.

Meta-analysis of repository data: impact of data regularization on NIMH schizophrenia linkage results.

Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.

Revisiting schizophrenia linkage data in the NIMH Repository: reanalysis of regularized data across multiple studies.

OBJECTIVE: The Combined Analysis of Psychiatric Studies (CAPS) project conducted extensive review and regularization across studies of all schizophrenia linkage data available as of 2011 from the National Institute of Mental Health-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI). The authors reanalyzed the data using statistical methods tailored to accumulation of evidence across multiple, potentially highly heterogeneous, sets of data. METHOD: Data were subdivided based on contributing study, major population group, and presence or absence within families of schizophrenia with a substantial affective component. The posterior probability of linkage (PPL) statistical framework was used to sequentially update linkage evidence across these data subsets (omnibus results). RESULTS: While some loci previously implicated using the HGI data were also identified in the present omnibus analysis (2q36.1, 15q23), others were not. Several loci were found that had not previously been reported in the HGI samples but are supported by independent linkage or association studies (3q28, 12q23.1, 11p11.2, Xq26.1). Not surprisingly, differences were seen across population groups. Of particular interest are signals on 11p15.3, 11p11.2, and Xq26.1, for which data from families with a substantial affective component support linkage while data from the remaining families provide evidence against linkage. All three of these loci overlap with loci reported in independent studies of bipolar disorder or mixed bipolar-schizophrenia samples. CONCLUSIONS: Public data repositories provide the opportunity to leverage large multisite data sets for studying complex disorders. Analysis with a statistical method specifically designed for such data enables us to extract new information from an existing data resource.

A genome scan for loci shared by autism spectrum disorder and language impairment.

OBJECTIVE: The authors conducted a genetic linkage study of families that have both autism spectrum disorder (ASD) and language-impaired probands to find common communication impairment loci. The hypothesis was that these families have a high genetic loading for impairments in language ability, thus influencing the language and communication deficits of the family members with ASD. Comprehensive behavioral phenotyping of the families also enabled linkage analysis of quantitative measures, including normal, subclinical, and disordered variation in all family members for the three general autism symptom domains: social, communication, and compulsive behaviors. METHOD: The primary linkage analysis coded persons with either ASD or specific language impairment as "affected." The secondary linkage analysis consisted of quantitative metrics of autism-associated behaviors capturing normal to clinically severe variation, measured in all family members. RESULTS: Linkage to language phenotypes was established at two novel chromosomal loci, 15q23-26 and 16p12. The secondary analysis of normal and disordered quantitative variation in social and compulsive behaviors established linkage to two loci for social behaviors (at 14q and 15q) and one locus for repetitive behaviors (at 13q). CONCLUSION: These data indicate shared etiology of ASD and specific language impairment at two novel loci. Additionally, nonlanguage phenotypes based on social aloofness and rigid personality traits showed compelling evidence for linkage in this study group. Further genetic mapping is warranted at these loci.

Validation of a cost-efficient multi-purpose SNP panel for disease based research.

BACKGROUND: Here we present convergent methodologies using theoretical calculations, empirical assessment on in-house and publicly available datasets as well as in silico simulations, that validate a panel of SNPs for a variety of necessary tasks in human genetics disease research before resources are committed to larger-scale genotyping studies on those samples. While large-scale well-funded human genetic studies routinely have up to a million SNP genotypes, samples in a human genetics laboratory that are not yet part of such studies may be productively utilized in pilot projects or as part of targeted follow-up work though such smaller scale applications require at least some genome-wide genotype data for quality control purposes such as DNA "barcoding" to detect swaps or contamination issues, determining familial relationships between samples and correcting biases due to population effects such as population stratification in pilot studies. PRINCIPAL FINDINGS: Empirical performance in classification of relative types for any two given DNA samples (e.g., full siblings, parental, etc) indicated that for outbred populations the panel performs sufficiently to classify relationship in extended families and therefore also for smaller structures such as trios and for twin zygosity testing. Additionally, familial relationships do not significantly diminish the (mean match) probability of sharing SNP genotypes in pedigrees, further indicating the uniqueness of the "barcode." Simulation using these SNPs for an African American case-control disease association study demonstrated that population stratification, even in complex admixed samples, can be adequately corrected under a range of disease models using the SNP panel. CONCLUSION: The panel has been validated for use in a variety of human disease genetics research tasks including sample barcoding, relationship verification, population substructure detection and statistical correction. Given the ease of genotyping our specific assay contained herein, this panel represents a useful and economical panel for human geneticists.

Combined linkage and linkage disequilibrium analysis of a motor speech phenotype within families ascertained for autism risk loci.

Using behavioral and genetic information from the Autism Genetics Resource Exchange (AGRE) data set we developed phenotypes and investigated linkage and association for individuals with and without Autism Spectrum Disorders (ASD) who exhibit expressive language behaviors consistent with a motor speech disorder. Speech and language variables from Autism Diagnostic Interview-Revised (ADI-R) were used to develop a motor speech phenotype associated with non-verbal or unintelligible verbal behaviors (NVMSD:ALL) and a related phenotype restricted to individuals without significant comprehension difficulties (NVMSD:C). Using Affymetrix 5.0 data, the PPL framework was employed to assess the strength of evidence for or against trait-marker linkage and linkage disequilibrium (LD) across the genome. Ingenuity Pathway Analysis (IPA) was then utilized to identify potential genes for further investigation. We identified several linkage peaks based on two related language-speech phenotypes consistent with a potential motor speech disorder: chromosomes 1q24.2, 3q25.31, 4q22.3, 5p12, 5q33.1, 17p12, 17q11.2, and 17q22 for NVMSD:ALL and 4p15.2 and 21q22.2 for NVMSD:C. While no compelling evidence of association was obtained under those peaks, we identified several potential genes of interest using IPA. CONCLUSION: Several linkage peaks were identified based on two motor speech phenotypes. In the absence of evidence of association under these peaks, we suggest genes for further investigation based on their biological functions. Given that autism spectrum disorders are complex with a wide range of behaviors and a large number of underlying genes, these speech phenotypes may belong to a group of several that should be considered when developing narrow, well-defined, phenotypes in the attempt to reduce genetic heterogeneity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11689-010-9063-2) contains supplementary material, which is available to authorized users.

Genetic structures of copy number variants revealed by genotyping single sperm.

BACKGROUND: Copy number variants (CNVs) occupy a significant portion of the human genome and may have important roles in meiotic recombination, human genome evolution and gene expression. Many genetic diseases may be underlain by CNVs. However, because of the presence of their multiple copies, variability in copy numbers and the diploidy of the human genome, detailed genetic structure of CNVs cannot be readily studied by available techniques. METHODOLOGY/PRINCIPAL FINDINGS: Single sperm samples were used as the primary subjects for the study so that CNV haplotypes in the sperm donors could be studied individually. Forty-eight CNVs characterized in a previous study were analyzed using a microarray-based high-throughput genotyping method after multiplex amplification. Seventeen single nucleotide polymorphisms (SNPs) were also included as controls. Two single-base variants, either allelic or paralogous, could be discriminated for all markers. Microarray data were used to resolve SNP alleles and CNV haplotypes, to quantitatively assess the numbers and compositions of the paralogous segments in each CNV haplotype. CONCLUSIONS/SIGNIFICANCE: This is the first study of the genetic structure of CNVs on a large scale. Resulting information may help understand evolution of the human genome, gain insight into many genetic processes, and discriminate between CNVs and SNPs. The highly sensitive high-throughput experimental system with haploid sperm samples as subjects may be used to facilitate detailed large-scale CNV analysis.

Identification of a schizophrenia-associated functional noncoding variant in NOS1AP.

OBJECTIVE: The authors previously demonstrated significant association between markers within NOS1AP and schizophrenia in a set of Canadian families of European descent, as well as significantly increased expression in schizophrenia of NOS1AP in unrelated postmortem samples from the dorsolateral prefrontal cortex. In this study the authors sought to apply novel statistical methods and conduct additional biological experiments to isolate at least one risk allele within NOS1AP. METHOD: Using the posterior probability of linkage disequilibrium (PPLD) to measure the probability that a single nucleotide polymorphism (SNP) is in linkage disequilibrium with schizophrenia, the authors evaluated 60 SNPs from NOS1AP in 24 Canadian families demonstrating linkage and association to this region. SNPs exhibiting strong evidence of linkage disequilibrium were tested for regulatory function by luciferase reporter assay. Two human neural cell lines (SK-N-MC and PFSK-1) were transfected with a vector containing each allelic variant of the SNP, the NOS1AP promoter, and a luciferase gene. Alleles altering expression were further assessed for binding of nuclear proteins by electrophoretic mobility shift assay. RESULTS: Three SNPs produced PPLDs >40%. One of them, rs12742393, demonstrated significant allelic expression differences in both cell lines tested. The allelic variation at this SNP altered the affinity of nuclear protein binding to this region of DNA. CONCLUSIONS: The A allele of rs12742393 appears to be a risk allele associated with schizophrenia that acts by enhancing transcription factor binding and increasing gene expression.

A biotin interference assay highlights two different asymmetric interaction profiles for lambda integrase arm-type binding sites in integrative versus excisive recombination.

The site-specific recombinase integrase encoded by bacteriophage lambda promotes integration and excision of the viral chromosome into and out of its Escherichia coli host chromosome through a Holliday junction recombination intermediate. This intermediate contains an integrase tetramer bound via its catalytic carboxyl-terminal domains to the four "core-type" sites of the Holliday junction DNA and via its amino-terminal domains to distal "arm-type" sites. The two classes of integrase binding sites are brought into close proximity by an ensemble of accessory proteins that bind and bend the intervening DNA. We have used a biotin interference assay that probes the requirement for major groove protein binding at specified DNA loci in conjunction with DNA protection, gel mobility shift, and genetic experiments to test several predictions of the models derived from the x-ray crystal structures of minimized and symmetrized surrogates of recombination intermediates lacking the accessory proteins and their cognate DNA targets. Our data do not support the predictions of "non-canonical" DNA targets for the N-domain of integrase, and they indicate that the complexes used for x-ray crystallography are more appropriate for modeling excisive rather than integrative recombination intermediates. We suggest that the difference in the asymmetric interaction profiles of the N-domains and arm-type sites in integrative versus excisive recombinogenic complexes reflects the regulation of recombination, whereas the asymmetry of these patterns within each reaction contributes to directionality.