Search

IntroductionLate-onset Alzheimer's disease (LOAD) has a strong genetic component. Participants in Long-Life Family Study (LLFS) exhibit delayed onset of dementia, offering a unique opportunity to investigate LOAD genetics.MethodsWe conducted a whole genome sequence analysis of 3475 LLFS members. Genetic associations were examined in six independent studies (N = 14,260) with a wide range of LOAD risk. Association analysis in a sub-sample of the LLFS cohort (N = 1739) evaluated the association of LOAD variants with beta amyloid (Aβ) levels.ResultsWe identified several single nucleotide polymorphisms (SNPs) in tight linkage disequilibrium within the MTUS2 gene associated with LOAD (rs73154407, p = 7.6 × 10-9). Association of MTUS2 variants with LOAD was observed in the five independent studies and was significantly stronger within high levels of Aβ42/40 ratio compared to lower amyloid.DiscussionMTUS2 encodes a microtubule associated protein implicated in the development and function of the nervous system, making it a plausible candidate to investigate LOAD biology.HighlightsLong-Life Family Study (LLFS) families may harbor late onset Alzheimer's dementia (LOAD) variants. LLFS whole genome sequence analysis identified MTUS2 gene variants associated with LOAD. The observed LLFS variants generalized to cohorts with wide range of LOAD risk. The association of MTUS2 with LOAD was stronger within high levels of beta amyloid. Our results provide evidence for MTUS2 gene as a novel LOAD candidate locus.

Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial BLAS library implements key computational primitives for rapid development of combinatorial algorithms in distributed-memory systems. During the decade since its first introduction, the Combinatorial BLAS library has evolved and expanded significantly. This article details many of the key technical features of Combinatorial BLAS version 2.0, such as communication avoidance, hierarchical parallelism via in-node multithreading, accelerator support via GPU kernels, generalized semiring support, implementations of key data structures and functions, and scalable distributed I/O operations for human-readable files. Our article also presents several rules of thumb for choosing the right data structures and functions in Combinatorial BLAS 2.0, under various common application scenarios.

Each additional copy of the apolipoprotein E4 (APOE4) allele is associated with a higher risk of Alzheimer's dementia, while the APOE2 allele is associated with a lower risk of Alzheimer's dementia, it is not yet known whether APOE2 homozygotes have a particularly low risk. We generated Alzheimer's dementia odds ratios and other findings in more than 5,000 clinically characterized and neuropathologically characterized Alzheimer's dementia cases and controls. APOE2/2 was associated with a low Alzheimer's dementia odds ratios compared to APOE2/3 and 3/3, and an exceptionally low odds ratio compared to APOE4/4, and the impact of APOE2 and APOE4 gene dose was significantly greater in the neuropathologically confirmed group than in more than 24,000 neuropathologically unconfirmed cases and controls. Finding and targeting the factors by which APOE and its variants influence Alzheimer's disease could have a major impact on the understanding, treatment and prevention of the disease.

Each additional copy of the apolipoprotein E4 (APOE4) allele is associated with a higher risk of Alzheimer's dementia, while the APOE2 allele is associated with a lower risk of Alzheimer's dementia, it is not yet known whether APOE2 homozygotes have a particularly low risk. We generated Alzheimer's dementia odds ratios and other findings in more than 5,000 clinically characterized and neuropathologically characterized Alzheimer's dementia cases and controls. APOE2/2 was associated with a low Alzheimer's dementia odds ratios compared to APOE2/3 and 3/3, and an exceptionally low odds ratio compared to APOE4/4, and the impact of APOE2 and APOE4 gene dose was significantly greater in the neuropathologically confirmed group than in more than 24,000 neuropathologically unconfirmed cases and controls. Finding and targeting the factors by which APOE and its variants influence Alzheimer's disease could have a major impact on the understanding, treatment and prevention of the disease.

IntroductionGenetic loci for Alzheimer's disease (AD) have been identified in whites of European ancestry, but the genetic architecture of AD among other populations is less understood.MethodsWe conducted a transethnic genome-wide association study (GWAS) for late-onset AD in Stage 1 sample including whites of European Ancestry, African-Americans, Japanese, and Israeli-Arabs assembled by the Alzheimer's Disease Genetics Consortium. Suggestive results from Stage 1 from novel loci were followed up using summarized results in the International Genomics Alzheimer's Project GWAS dataset.ResultsGenome-wide significant (GWS) associations in single-nucleotide polymorphism (SNP)-based tests (P

We study the performance of linear solvers for graph Laplacians based on thecombinatorial cycle adjustment methodology proposed by[Kelner-Orecchia-Sidford-Zhu STOC-13]. The approach finds a dual flow solutionto this linear system through a sequence of flow adjustments along cycles. Westudy both data structure oriented and recursive methods for handling theseadjustments. The primary difficulty faced by this approach, updating and querying longcycles, motivated us to study an important special case: instances where allcycles are formed by fundamental cycles on a length $n$ path. Our methodsdemonstrate significant speedups over previous implementations, and arecompetitive with standard numerical routines.

Recommender system data presents unique challenges to the data mining,machine learning, and algorithms communities. The high missing data rate, incombination with the large scale and high dimensionality that is typical ofrecommender systems data, requires new tools and methods for efficient dataanalysis. Here, we address the challenge of evaluating similarity between twousers in a recommender system, where for each user only a small set of ratingsis available. We present a new similarity score, that we call LiRa, based on astatistical model of user similarity, for large-scale, discrete valued datawith many missing values. We show that this score, based on a ratio oflikelihoods, is more effective at identifying similar users than traditionalsimilarity scores in user-based collaborative filtering, such as the Pearsoncorrelation coefficient. We argue that our approach has significant potentialto improve both accuracy and scalability in collaborative filtering.

Recommender system data presents unique challenges to the data mining,machine learning, and algorithms communities. The high missing data rate, incombination with the large scale and high dimensionality that is typical ofrecommender systems data, requires new tools and methods for efficient dataanalysis. Here, we address the challenge of evaluating similarity between twousers in a recommender system, where for each user only a small set of ratingsis available. We present a new similarity score, that we call LiRa, based on astatistical model of user similarity, for large-scale, discrete valued datawith many missing values. We show that this score, based on a ratio oflikelihoods, is more effective at identifying similar users than traditionalsimilarity scores in user-based collaborative filtering, such as the Pearsoncorrelation coefficient. We argue that our approach has significant potentialto improve both accuracy and scalability in collaborative filtering.

ObjectiveTo identify a causative variant(s) that may contribute to Alzheimer disease (AD) in African Americans (AA) in the ATP-binding cassette, subfamily A (ABC1), member 7 (ABCA7) gene, a known risk factor for late-onset AD.MethodsCustom capture sequencing was performed on ∼150 kb encompassing ABCA7 in 40 AA cases and 37 AA controls carrying the AA risk allele (rs115550680). Association testing was performed for an ABCA7 deletion identified in large AA data sets (discovery n = 1,068; replication n = 1,749) and whole exome sequencing of Caribbean Hispanic (CH) AD families.ResultsA 44-base pair deletion (rs142076058) was identified in all 77 risk genotype carriers, which shows that the deletion is in high linkage disequilibrium with the risk allele. The deletion was assessed in a large data set (531 cases and 527 controls) and, after adjustments for age, sex, and APOE status, was significantly associated with disease (p = 0.0002, odds ratio [OR] = 2.13 [95% confidence interval (CI): 1.42-3.20]). An independent data set replicated the association (447 cases and 880 controls, p = 0.0117, OR = 1.65 [95% CI: 1.12-2.44]), and joint analysis increased the significance (p = 1.414 × 10(-5), OR = 1.81 [95% CI: 1.38-2.37]). The deletion is common in AA cases (15.2%) and AA controls (9.74%), but in only 0.12% of our non-Hispanic white cohort. Whole exome sequencing of multiplex, CH families identified the deletion cosegregating with disease in a large sibship. The deleted allele produces a stable, detectable RNA strand and is predicted to result in a frameshift mutation (p.Arg578Alafs) that could interfere with protein function.ConclusionsThis common ABCA7 deletion could represent an ethnic-specific pathogenic alteration in AD.

ObjectiveTo identify a causative variant(s) that may contribute to Alzheimer disease (AD) in African Americans (AA) in the ATP-binding cassette, subfamily A (ABC1), member 7 (ABCA7) gene, a known risk factor for late-onset AD.MethodsCustom capture sequencing was performed on ∼150 kb encompassing ABCA7 in 40 AA cases and 37 AA controls carrying the AA risk allele (rs115550680). Association testing was performed for an ABCA7 deletion identified in large AA data sets (discovery n = 1,068; replication n = 1,749) and whole exome sequencing of Caribbean Hispanic (CH) AD families.ResultsA 44-base pair deletion (rs142076058) was identified in all 77 risk genotype carriers, which shows that the deletion is in high linkage disequilibrium with the risk allele. The deletion was assessed in a large data set (531 cases and 527 controls) and, after adjustments for age, sex, and APOE status, was significantly associated with disease (p = 0.0002, odds ratio [OR] = 2.13 [95% confidence interval (CI): 1.42-3.20]). An independent data set replicated the association (447 cases and 880 controls, p = 0.0117, OR = 1.65 [95% CI: 1.12-2.44]), and joint analysis increased the significance (p = 1.414 × 10(-5), OR = 1.81 [95% CI: 1.38-2.37]). The deletion is common in AA cases (15.2%) and AA controls (9.74%), but in only 0.12% of our non-Hispanic white cohort. Whole exome sequencing of multiplex, CH families identified the deletion cosegregating with disease in a large sibship. The deleted allele produces a stable, detectable RNA strand and is predicted to result in a frameshift mutation (p.Arg578Alafs) that could interfere with protein function.ConclusionsThis common ABCA7 deletion could represent an ethnic-specific pathogenic alteration in AD.

Alzheimer's disease (AD) is a complex genetic disorder with no effective treatments. More than 20 common markers have been identified, which are associated with AD. Recently, several rare variants have been identified in Amyloid Precursor Protein (APP), Triggering Receptor Expressed On Myeloid Cells 2 (TREM2) and Unc-5 Netrin Receptor C (UNC5C) that affect risk for AD. Despite the many successes, the genetic architecture of AD remains unsolved. We used Genome-wide Complex Trait Analysis to (1) estimate phenotypic variance explained by genetics; (2) calculate genetic variance explained by known AD single nucleotide polymorphisms (SNPs); and (3) identify the genomic locations of variation that explain the remaining unexplained genetic variance. In total, 53.24% of phenotypic variance is explained by genetics, but known AD SNPs only explain 30.62% of the genetic variance. Of the unexplained genetic variance, approximately 41% is explained by unknown SNPs in regions adjacent to known AD SNPs, and the remaining unexplained genetic variance outside these regions.

Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices (semiring operations). The user can also customize existing graph algorithms by writing filters. Although the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. Overall, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs with hundreds of millions of edges and scaling to thousands of processors for graphs.

We set out to determine whether expansions in the C9ORF72 repeat found in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) families are associated with Parkinson disease (PD). We determined the repeat size in a total of 889 clinically ascertained patients (including PD and essential tremor plus Parkinsonism (ETP)) and 1144 controls using a repeat-primed PCR assay. We found that large C9ORF72 repeat expansions (>30 repeats) were not contributing to PD risk. However, PD and ETP cases had a significant increase in intermediate (>20 to 30+) repeat copies compared to controls. Overall, 14 cases (13 PD, 1 ETP) and three controls had >20 repeat copies (Fisher's exact test p = 0.002). Further, seven cases and no controls had >23 repeat copies (p = 0.003). Our results suggest that intermediate copy numbers of the C9ORF72 repeat contribute to risk for PD and ETP. This also suggests that PD, ALS and FTD share some pathophysiological mechanisms of disease. Further studies are needed to elucidate the contribution of the C9ORF72 repeat in the overall PD population and to determine whether other common genetic risk factors exist between these neurodegenerative disorders.

We set out to determine whether expansions in the C9ORF72 repeat found in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) families are associated with Parkinson disease (PD). We determined the repeat size in a total of 889 clinically ascertained patients (including PD and essential tremor plus Parkinsonism (ETP)) and 1144 controls using a repeat-primed PCR assay. We found that large C9ORF72 repeat expansions (>30 repeats) were not contributing to PD risk. However, PD and ETP cases had a significant increase in intermediate (>20 to 30+) repeat copies compared to controls. Overall, 14 cases (13 PD, 1 ETP) and three controls had >20 repeat copies (Fisher's exact test p = 0.002). Further, seven cases and no controls had >23 repeat copies (p = 0.003). Our results suggest that intermediate copy numbers of the C9ORF72 repeat contribute to risk for PD and ETP. This also suggests that PD, ALS and FTD share some pathophysiological mechanisms of disease. Further studies are needed to elucidate the contribution of the C9ORF72 repeat in the overall PD population and to determine whether other common genetic risk factors exist between these neurodegenerative disorders.

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry attributes of various types. Analytic queries on semantic graphs typically depend on the values of these attributes, thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, is customizable in two ways. First, the user can write custom graph algorithms by specifying operations between edges and vertices. These programmer-specified operations are called semiring operations due to KDT's underlying linear-algebraic abstractions. Second, the user can customize existing graph algorithms by writing filters that return true for those vertices and edges the user wants to retain during algorithm execution. For high productivity, both semiring operations and filters are written in a high-level language, resulting in relatively low performance due to the bottleneck of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the up call into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine, and show our approach enables users to write in high-level languages and still obtain the high performance of low-level code. We also present a new roofline model for graph traversals, and show that our high-performance implementations do not significantly deviate from the roofline. Overall, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs. © 2013 IEEE.

Alzheimer disease (AD) is a devastating neurodegenerative disease affecting more than five million Americans. In this study, we have used updated genetic linkage data from chromosome 10 in combination with expression data from serial analysis of gene expression to choose a new set of thirteen candidate genes for genetic analysis in late onset Alzheimer disease (LOAD). Results in this study identify the KIAA1462 locus as a candidate locus for LOAD in APOE4 carriers. Two genes exist at this locus, KIAA1462, a gene associated with coronary artery disease, and "rokimi", encoding an untranslated spliced RNA The genetic architecture at this locus suggests that the gene product important in this association is either "rokimi", or a different isoform of KIAA1462 than the isoform that is important in cardiovascular disease. Expression data suggests that isoform f of KIAA1462 is a more attractive candidate for association with LOAD in APOE4 carriers than "rokimi" which had no detectable expression in brain.

Alzheimer disease (AD) is a devastating neurodegenerative disease affecting more than five million Americans. In this study, we have used updated genetic linkage data from chromosome 10 in combination with expression data from serial analysis of gene expression to choose a new set of thirteen candidate genes for genetic analysis in late onset Alzheimer disease (LOAD). Results in this study identify the KIAA1462 locus as a candidate locus for LOAD in APOE4 carriers. Two genes exist at this locus, KIAA1462, a gene associated with coronary artery disease, and "rokimi", encoding an untranslated spliced RNA The genetic architecture at this locus suggests that the gene product important in this association is either "rokimi", or a different isoform of KIAA1462 than the isoform that is important in cardiovascular disease. Expression data suggests that isoform f of KIAA1462 is a more attractive candidate for association with LOAD in APOE4 carriers than "rokimi" which had no detectable expression in brain.

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry \attributes" of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must either view the graph through a filter that passes only those individual vertices and edges of interest, or else must first materialize a subgraph or subgraphs consisting of only the vertices and edges of interest. The filtered approach is superior due to its generality, ease of use, and memory efficiency, but may carry a performance cost. In the Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, the user writes filters in a high-level language, but those filters result in relatively low performance due to the bottleneck of having to call into the Python interpreter for each edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance C++ /MPI Combinatorial BLAS engine, and show that the productivity gained by using a high-level filtering language comes without sacrificing performance. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM).

ObjectiveRecent genome-wide association studies (GWAS) of late-onset Alzheimer disease (LOAD) identified 9 novel risk loci. Discovery of functional variants within genes at these loci is required to confirm their role in Alzheimer disease (AD). Single nucleotide polymorphisms that influence gene expression (eSNPs) constitute an important class of functional variants. We therefore investigated the influence of the novel LOAD risk loci on human brain gene expression.MethodsWe measured gene expression levels in the cerebellum and temporal cortex of autopsied AD subjects and those with other brain pathologies (∼400 total subjects). To determine whether any of the novel LOAD risk variants are eSNPs, we tested their cis-association with expression of 6 nearby LOAD candidate genes detectable in human brain (ABCA7, BIN1, CLU, MS4A4A, MS4A6A, PICALM) and an additional 13 genes ±100 kb of these SNPs. To identify additional eSNPs that influence brain gene expression levels of the novel candidate LOAD genes, we identified SNPs ±100 kb of their location and tested for cis-associations.ResultsCLU rs11136000 (p = 7.81 × 10(-4)) and MS4A4A rs2304933/rs2304935 (p = 1.48 × 10(-4)-1.86 × 10(-4)) significantly influence temporal cortex expression levels of these genes. The LOAD-protective CLU and risky MS4A4A locus alleles associate with higher brain levels of these genes. There are other cis-variants that significantly influence brain expression of CLU and ABCA7 (p = 4.01 × 10(-5)-9.09 × 10(-9)), some of which also associate with AD risk (p = 2.64 × 10(-2)-6.25 × 10(-5)).ConclusionsCLU and MS4A4A eSNPs may at least partly explain the LOAD risk association at these loci. CLU and ABCA7 may harbor additional strong eSNPs. These results have implications in the search for functional variants at the novel LOAD risk loci.

Abstract Background Autism spectrum disorders (ASD) represent a group of neurodevelopmental disorders characterized by a core set of social-communicative and behavioral impairments. Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the brain, acting primarily via the GABA receptors (GABR). Multiple lines of evidence, including altered GABA and GABA receptor expression in autistic patients, indicate that the GABAergic system may be involved in the etiology of autism. Methods As copy number variations (CNVs), particularly rare and de novo CNVs, have now been implicated in ASD risk, we examined the GABA receptors and genes in related pathways for structural variation that may be associated with autism. We further extended our candidate gene set to include 19 genes and regions that had either been directly implicated in the autism literature or were directly related (via function or ancestry) to these primary candidates. For the high resolution CNV screen we employed custom-designed 244 k comparative genomic hybridization (CGH) arrays. Collectively, our probes spanned a total of 11 Mb of GABA-related and additional candidate regions with a density of approximately one probe every 200 nucleotides, allowing a theoretical resolution for detection of CNVs of approximately 1 kb or greater on average. One hundred and sixty-eight autism cases and 149 control individuals were screened for structural variants. Prioritized CNV events were confirmed using quantitative PCR, and confirmed loci were evaluated on an additional set of 170 cases and 170 control individuals that were not included in the original discovery set. Loci that remained interesting were subsequently screened via quantitative PCR on an additional set of 755 cases and 1,809 unaffected family members. Results Results include rare deletions in autistic individuals at JAKMIP1, NRXN1, Neuroligin4Y, OXTR, and ABAT. Common insertion/deletion polymorphisms were detected at several loci, including GABBR2 and NRXN3. Overall, statistically significant enrichment in affected vs. unaffected individuals was observed for NRXN1 deletions. Conclusions These results provide additional support for the role of rare structural variation in ASD.

The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study of late-onset Alzheimer disease using a three-stage design consisting of a discovery stage (stage 1) and two replication stages (stages 2 and 3). Both joint analysis and meta-analysis approaches were used. We obtained genome-wide significant results at MS4A4A (rs4938933; stages 1 and 2, meta-analysis P (P(M)) = 1.7 × 10(-9), joint analysis P (P(J)) = 1.7 × 10(-9); stages 1, 2 and 3, P(M) = 8.2 × 10(-12)), CD2AP (rs9349407; stages 1, 2 and 3, P(M) = 8.6 × 10(-9)), EPHA1 (rs11767557; stages 1, 2 and 3, P(M) = 6.0 × 10(-10)) and CD33 (rs3865444; stages 1, 2 and 3, P(M) = 1.6 × 10(-9)). We also replicated previous associations at CR1 (rs6701713; P(M) = 4.6 × 10(-10), P(J) = 5.2 × 10(-11)), CLU (rs1532278; P(M) = 8.3 × 10(-8), P(J) = 1.9 × 10(-8)), BIN1 (rs7561528; P(M) = 4.0 × 10(-14), P(J) = 5.2 × 10(-14)) and PICALM (rs561655; P(M) = 7.0 × 10(-11), P(J) = 1.0 × 10(-10)), but not at EXOC3L2, to late-onset Alzheimer's disease susceptibility.

The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study of late-onset Alzheimer disease using a three-stage design consisting of a discovery stage (stage 1) and two replication stages (stages 2 and 3). Both joint analysis and meta-analysis approaches were used. We obtained genome-wide significant results at MS4A4A (rs4938933; stages 1 and 2, meta-analysis P (P(M)) = 1.7 × 10(-9), joint analysis P (P(J)) = 1.7 × 10(-9); stages 1, 2 and 3, P(M) = 8.2 × 10(-12)), CD2AP (rs9349407; stages 1, 2 and 3, P(M) = 8.6 × 10(-9)), EPHA1 (rs11767557; stages 1, 2 and 3, P(M) = 6.0 × 10(-10)) and CD33 (rs3865444; stages 1, 2 and 3, P(M) = 1.6 × 10(-9)). We also replicated previous associations at CR1 (rs6701713; P(M) = 4.6 × 10(-10), P(J) = 5.2 × 10(-11)), CLU (rs1532278; P(M) = 8.3 × 10(-8), P(J) = 1.9 × 10(-8)), BIN1 (rs7561528; P(M) = 4.0 × 10(-14), P(J) = 5.2 × 10(-14)) and PICALM (rs561655; P(M) = 7.0 × 10(-11), P(J) = 1.0 × 10(-10)), but not at EXOC3L2, to late-onset Alzheimer's disease susceptibility.

Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.

Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.