SummaryAlthough autism is one of the most heritable neuropsychiatric disorders, its underlying genetic architecture has largely eluded description. To comprehensively examine the hypothesis that common variation is important in autism, we performed a genome-wide association study (GWAS) using a discovery dataset of 438 autistic Caucasian families and the Illumina Human 1M beadchip. 96 single nucleotide polymorphisms (SNPs) demonstrated strong association with autism risk (p-value < 0.0001). The validation of the top 96 SNPs was performed using an independent dataset of 487 Caucasian autism families genotyped on the 550K Illumina BeadChip. A novel region on chromosome 5p14.1 showed significance in both the discovery and validation datasets. Joint analysis of all SNPs in this region identified 8 SNPs having improved p-values (3.24E-04 to 3.40E-06) than in either dataset alone. Our findings demonstrate that in addition to multiple rare variations, part of the complex genetic architecture of autism involves common variation.

BackgroundGenome-wide Association Studies (GWAS) have proved invaluable for the identification of disease susceptibility genes. However, the prioritization of candidate genes and regions for follow-up studies often proves difficult due to false-positive associations caused by statistical noise and multiple-testing. In order to address this issue, we propose the novel GWAS noise reduction (GWAS-NR) method as a way to increase the power to detect true associations in GWAS, particularly in complex diseases such as autism.MethodsGWAS-NR utilizes a linear filter to identify genomic regions demonstrating correlation among association signals in multiple datasets. We used computer simulations to assess the ability of GWAS-NR to detect association against the commonly used joint analysis and Fisher's methods. Furthermore, we applied GWAS-NR to a family-based autism GWAS of 597 families and a second existing autism GWAS of 696 families from the Autism Genetic Resource Exchange (AGRE) to arrive at a compendium of autism candidate genes. These genes were manually annotated and classified by a literature review and functional grouping in order to reveal biological pathways which might contribute to autism aetiology.ResultsComputer simulations indicate that GWAS-NR achieves a significantly higher classification rate for true positive association signals than either the joint analysis or Fisher's methods and that it can also achieve this when there is imperfect marker overlap across datasets or when the closest disease-related polymorphism is not directly typed. In two autism datasets, GWAS-NR analysis resulted in 1535 significant linkage disequilibrium (LD) blocks overlapping 431 unique reference sequencing (RefSeq) genes. Moreover, we identified the nearest RefSeq gene to the non-gene overlapping LD blocks, producing a final candidate set of 860 genes. Functional categorization of these implicated genes indicates that a significant proportion of them cooperate in a coherent pathway that regulates the directional protrusion of axons and dendrites to their appropriate synaptic targets.ConclusionsAs statistical noise is likely to particularly affect studies of complex disorders, where genetic heterogeneity or interaction between genes may confound the ability to detect association, GWAS-NR offers a powerful method for prioritizing regions for follow-up studies. Applying this method to autism datasets, GWAS-NR analysis indicates that a large subset of genes involved in the outgrowth and guidance of axons and dendrites is implicated in the aetiology of autism.

Autism spectrum disorders (ASDs) are highly heritable, yet relatively few associated genetic loci have been replicated. Copy number variations (CNVs) have been implicated in autism; however, the majority of loci contribute to <1% of the disease population. Therefore, independent studies are important to refine associated CNV regions and discover novel susceptibility genes. In this study, a genome-wide SNP array was utilized for CNV detection by two distinct algorithms in a European ancestry case-control data set. We identify a significantly higher burden in the number and size of deletions, and disrupting more genes in ASD cases. Moreover, 18 deletions larger than 1 Mb were detected exclusively in cases, implicating novel regions at 2q22.1, 3p26.3, 4q12 and 14q23. Case-specific CNVs provided further evidence for pathways previously implicated in ASDs, revealing new candidate genes within the GABAergic signaling and neural development pathways. These include DBI, an allosteric binder of GABA receptors, GABARAPL1, the GABA receptor-associated protein, and SLC6A11, a postsynaptic GABA transporter. We also identified CNVs in COBL, deletions of which cause defects in neuronal cytoskeleton morphogenesis in model vertebrates, and DNER, a neuron-specific Notch ligand required for cerebellar development. Moreover, we found evidence of genetic overlap between ASDs and other neurodevelopmental and neuropsychiatric diseases. These genes include glutamate receptors (GRID1, GRIK2 and GRIK4), synaptic regulators (NRXN3, SLC6A8 and SYN3), transcription factor (ZNF804A) and RNA-binding protein FMR1. Taken together, these CNVs may be a few of the missing pieces of ASD heritability and lead to discovering novel etiological mechanisms.

Several genome-wide screens have indicated the presence of an autism susceptibility locus within the distal long arm of chromosome 7 (7q). Mapping at 7q22 within this region is the candidate gene reelin (RELN). RELN encodes a signaling protein that plays a pivotal role in the migration of several neuronal cell types and in the development of neural connections. Given these neurodevelopmental functions, recent reports that RELN influences genetic risk for autism are of significant interest. The total data set consists of 218 Caucasian families collected by our group, 85 Caucasian families collected by AGRE, and 68 Caucasian families collected at Tufts University were tested for genetic association of RELN variants to autism. Markers included five single-nucleotide polymorphisms (SNPs) and a repeat in the 5 0 -untranslated region (5 0 -UTR). Tests for association in Duke and AGRE families were also performed on four additional SNPs in the genes PSMC2 and ORC5L, which flank RELN. Familybased association analyses (PDT, Geno-PDT, and FBAT) were used to test for association of single-locus markers and multilocus haplotypes with autism. The most significant association identified from this combined data set was for the 5 0 -UTR repeat (PDT P-value ¼ 0.002). These analyses show the potential of RELN as an important contributor to genetic risk in autism.

Identification of mutations at familial loci for amyotrophic lateral sclerosis (ALS) has provided novel insights into the aetiology of this rapidly progressing fatal neurodegenerative disease. However, genome-wide association studies (GWAS) of the more common (∼90%) sporadic form have been less successful with the exception of the replicated locus at 9p21.2. To identify new loci associated with disease susceptibility, we have established the largest association study in ALS to date and undertaken a GWAS meta-analytical study combining 3959 newly genotyped Italian individuals (1982 cases and 1977 controls) collected by SLAGEN (Italian Consortium for the Genetics of ALS) together with samples from Netherlands, USA, UK, Sweden, Belgium, France, Ireland and Italy collected by ALSGEN (the International Consortium on Amyotrophic Lateral Sclerosis Genetics). We analysed a total of 13 225 individuals, 6100 cases and 7125 controls for almost 7 million single-nucleotide polymorphisms (SNPs). We identified a novel locus with genome-wide significance at 17q11.2 (rs34517613 with P = 1.11 × 10(-8); OR 0.82) that was validated when combined with genotype data from a replication cohort (P = 8.62 × 10(-9); OR 0.833) of 4656 individuals. Furthermore, we confirmed the previously reported association at 9p21.2 (rs3849943 with P = 7.69 × 10(-9); OR 1.16). Finally, we estimated the contribution of common variation to heritability of sporadic ALS as ∼12% using a linear mixed model accounting for all SNPs. Our results provide an insight into the genetic structure of sporadic ALS, confirming that common variation contributes to risk and that sufficiently powered studies can identify novel susceptibility loci.

Copy number variations (CNVs) are a major cause of genetic disruption in the human genome with far more nucleotides being altered by duplications and deletions than by single nucleotide polymorphisms (SNPs). In the multifaceted etiology of autism spectrum disorders (ASDs), CNVs appear to contribute significantly to our understanding of the pathogenesis of this complex disease. A unique resource of 42 extended ASD families was genotyped for over 1 million SNPs to detect CNVs that may contribute to ASD susceptibility. Each family has at least one avuncular or cousin pair with ASD. Families were then evaluated for co-segregation of CNVs in ASD patients. We identified a total of five deletions and seven duplications in eleven families that co-segregated with ASD. Two of the CNVs overlap with regions on 7p21.3 and 15q24.1 that have been previously reported in ASD individuals and two additional CNVs on 3p26.3 and 12q24.32 occur near regions associated with schizophrenia. These findings provide further evidence for the involvement of ICA1 and NXPH1 on 7p21.3 in ASD susceptibility and highlight novel ASD candidates, including CHL1, FGFBP3 and POUF41. These studies highlight the power of using extended families for gene discovery in traits with a complex etiology.

ObjectiveTo identify rare causal variants underlying known loci that segregate with late‐onset Alzheimer's disease (LOAD) in multiplex families.MethodsWe analyzed whole genome sequences (WGS) from 351 members of 67 Caribbean Hispanic (CH) families from Dominican Republic and New York multiply affected by LOAD. Members of 67 CH and additional 47 Caucasian families underwent WGS as a part of the Alzheimer's Disease Sequencing Project (ADSP). All members of 67 CH families, an additional 48 CH families and an independent CH case‐control cohort were subsequently genotyped for validation. Patients met criteria for LOAD, and controls were determined to be dementia free. We investigated rare variants segregating within families and gene‐based associations with disease within LOAD GWAS loci.ResultsA variant in AKAP9, p.R434W, segregated significantly with LOAD in two large families (OR = 5.77, 95% CI: 1.07–30.9, P = 0.041). In addition, missense mutations in MYRF and ASRGL1 under previously reported linkage peaks at 7q14.3 and 11q12.3 segregated completely in one family and in follow‐up genotyping both were nominally significant (P < 0.05). We also identified rare variants in a number of genes associated with LOAD in prior genome wide association studies, including CR1 (P = 0.049), BIN1 (P = 0.0098) and SLC24A4 (P = 0.040).Conclusions and RelevanceRare variants in multiple genes influence the risk of LOAD disease in multiplex families. These results suggest that rare variants may underlie loci identified in genome wide association studies.

SummaryGene-gene interactions are likely involved in many complex genetic disorders and new statistical approaches for detecting such interactions are needed. We propose a multi-analytic paradigm, relying on convergence of evidence across multiple analysis tools. Our paradigm tests for main and interactive effects, through allele, genotype and haplotype association. We applied our paradigm to genotype data from three GABA A receptor subunit genes (GABRB3, GABRA5, and GABRG3) on chromosome 15 in 470 Caucasian autism families. Previously implicated in autism, we hypothesized these genes interact to contribute to risk. We detected no evidence of main effects by allelic (PDT, FBAT) or genotypic (genotype-PDT) association at individual markers. However, three twomarker haplotypes in GABRG3 were significant (HBAT). We detected no significant multi-locus associations using genotype-PDT analysis or the EMDR data reduction program. However, consistent with the haplotype findings, the best single locus EMDR model selected a GABRG3 marker. Further, the best pairwise genotype-PDT result involved GABRB3 and GABRG3, and all multi-locus EMDR models also selected GABRB3 and GABRG3 markers. GABA receptor subunit genes do not significantly interact to contribute to autism risk in our overall data set. However, the consistency of results across analyses suggests that we have defined a useful framework for evaluating gene-gene interactions.