Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies.

Short-read genome sequencing (GS) holds the promise of becoming the primary diagnostic approach for the assessment of autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). However, few studies have comprehensively evaluated its performance against current standard-of-care diagnostic tests: karyotype, chromosomal microarray (CMA), and exome sequencing (ES). To assess the clinical utility of GS, we compared its diagnostic yield against these three tests in 1,612 quartet families including an individual with ASD and in 295 prenatal families. Our GS analytic framework identified a diagnostic variant in 7.8% of ASD probands, almost 2-fold more than CMA (4.3%) and 3-fold more than ES (2.7%). However, when we systematically captured copy-number variants (CNVs) from the exome data, the diagnostic yield of ES (7.4%) was brought much closer to, but did not surpass, GS. Similarly, we estimated that GS could achieve an overall diagnostic yield of 46.1% in unselected FSAs, representing a 17.2% increased yield over karyotype, 14.1% over CMA, and 4.1% over ES with CNV calling or 36.1% increase without CNV discovery. Overall, GS provided an added diagnostic yield of 0.4% and 0.8% beyond the combination of all three standard-of-care tests in ASD and FSAs, respectively. This corresponded to nine GS unique diagnostic variants, including sequence variants in exons not captured by ES, structural variants (SVs) inaccessible to existing standard-of-care tests, and SVs where the resolution of GS changed variant classification. Overall, this large-scale evaluation demonstrated that GS significantly outperforms each individual standard-of-care test while also outperforming the combination of all three tests, thus warranting consideration as the first-tier diagnostic approach for the assessment of ASD and FSAs.

Functional annotation of rare structural variation in the human brain.

Structural variants (SVs) contribute to many disorders, yet, functionally annotating them remains a major challenge. Here, we integrate SVs with RNA-sequencing from human post-mortem brains to quantify their dosage and regulatory effects. We show that genic and regulatory SVs exist at significantly lower frequencies than intergenic SVs. Functional impact of copy number variants (CNVs) stems from both the proportion of genic and regulatory content altered and loss-of-function intolerance of the gene. We train a linear model to predict expression effects of rare CNVs and use it to annotate regulatory disruption of CNVs from 14,891 independent genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders show significantly more extreme regulatory disruption scores and if rank ordered would be prioritized higher than using frequency or length alone. This work shows the deleteriousness of regulatory SVs, particularly those altering CTCF sites and provides a simple approach for functionally annotating the regulatory consequences of CNVs.

Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder.

Whole-genome sequencing (WGS) has facilitated the first genome-wide evaluations of the contribution of de novo noncoding mutations to complex disorders. Using WGS, we identified 255,106 de novo mutations among sample genomes from members of 1902 quartet families in which one child, but not a sibling or their parents, was affected by autism spectrum disorder (ASD). In contrast to coding mutations, no noncoding functional annotation category, analyzed in isolation, was significantly associated with ASD. Casting noncoding variation in the context of a de novo risk score across multiple annotation categories, however, did demonstrate association with mutations localized to promoter regions. We found that the strongest driver of this promoter signal emanates from evolutionarily conserved transcription factor binding sites distal to the transcription start site. These data suggest that de novo mutations in promoter regions, characterized by evolutionary and functional signatures, contribute to ASD.

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder.

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

Potential involvement of the interleukin-18 pathway in schizophrenia.

OBJECTIVE: Accumulating evidence implicates inflammatory cytokines in the development of psychiatric disorders, including schizophrenia (SZ). IL-18 is one of cytokines that plays a crucial role in immune response and neurodevelopment. We aimed to investigate potential genetic alterations of the cytokine system underpinning SZ. METHODS: We tested the association of genetic variants within the cytokine-cytokine receptor interaction (CCRI) pathway with SZ, using GWAS-derived data involving 768 adult SZ patients and 1348 controls, and replicated the association of IL18R1 rs1035130 with SZ in an independent sample of 1957 adult patients and 1509 controls. We compared expression levels of IL18, IL18R1 and IL18RAP in peripheral blood of a cohort of adolescent participants (<18 years), including 14 early-onset SZ patients and 13 healthy controls. Furthermore, we carried out a cis-eQTL (expression Quantitative Trait Loci) and a cis-mQTL (Methylation Quantitative Trait Loci) analysis for IL18R1 rs1035130. RESULTS: In the discovery stage, we detected association signals within two IL18 pathway genes, IL18R1 and IL18RAP, with the most significant marker being IL18R1 rs1035130 (P = 1.84E-7, OR = 0.70). In the validation stage, we found rs1035130 was associated with SZ (P = 0.028, OR = 0.89). Expressions of IL18 and IL18R1 were altered in blood of SZ patients compared with 13 controls. Furthermore, cis-QTL analyses indicated that rs1035130 was associated with an eQTL and 5 mQTLs. CONCLUSION: Our findings suggest the alteration of IL18 pathway may contribute to the psychopathology of SZ.

Family-based tests applied to extended pedigrees identify rare variants related to hypertension.

The application of family-based tests to whole-genome sequenced data provides a new window on the role of rare variant alleles in the etiology of disease. By applying family-based tests to these data, we can now identify rare variants associated with disease. Approaches for common variants, by contrast, require large sample sizes for power, and are powerless when faced with rare variants. When we tested Yip et al's 2011 family-based association tests for rare variants on pedigrees from the Genetic Analysis Workshop 18, we found that weighted collapsing methods generally have more power than unweighted methods, but are more prone to type I errors. We then evaluated a sliding window modification of the weighted family-based association tests for rare variants method. Although this modification inflates the rate of false positives, it significantly increases the power of family-based association tests for rare variants to identify causal rare variants.

New insights into the genetic mechanism of IQ in autism spectrum disorders.

Autism spectrum disorders (ASD) comprise a number of underlying sub-types with various symptoms and presumably different genetic causes. One important difference between these sub-phenotypes is IQ. Some forms of ASD such as Asperger's have relatively intact intelligence while the majority does not. In this study, we explored the role of genetic factors that might account for this difference. Using a case-control study based on IQ status in 1657 ASD probands, we analyzed both common and rare variants provided by the Autism Genome Project (AGP) consortium via dbGaP (database of Genotypes and Phenotypes). We identified a set of genes, among them HLA-DRB1 and KIAA0319L, which are strongly associated with IQ within a population of ASD patients.