Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls

Identification of potentially pathogenic variants for autism spectrum disorders using gene-burden analysis

Gene- burden analyses have lately become a very successful way for the identification of genes carrying risk variants underlying the analysed disease. This approach is also suitable for complex disorders like autism spectrum disorder (ASD). The gene-burden analysis using Testing Rare Variants with Public Data (TRAPD) software was conducted on whole exome sequencing data of Slovenian patients with ASD to determine potentially novel disease risk variants in known ASD-associated genes as well as in others. To choose the right control group for testing, principal component analysis based on the 1000 Genomes and ASD cohort samples was conducted. The subsequent protein structure and ligand binding analysis usingI-TASSER package were performed to detect changes in protein structure and ligand binding to determine a potential pathogenic consequence of observed mutation. The obtained results demonstrate an association of two variants-p.Glu198Lys (PPP2R5D:c.592G>A) and p.Arg253Gln (PPP2R5D:c.758G>A) with the ASD. Substitution p.Glu198Lys (PPP2R5D:c.592G>A) is a variant, previously described as pathogenic in association with ASD combined with intellectual disability, whereas p.Arg253Gln (PPP2R5D:c.758G>A) has not been described as an ASD-associated pathogenic variant yet. The results indicate that the filtering process was suitable and could be used in the future for detection of novel pathogenic variants when analysing groups of ASD patients.

Gene Variants Involved in Nonsense-Mediated mRNA Decay Suggest a Role in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with unclear etiology. Many genes have been associated with ASD risk, but the underlying mechanisms are still poorly understood. An important post-transcriptional regulatory mechanism that plays an essential role during neurodevelopment, the Nonsense-Mediated mRNA Decay (NMD) pathway, may contribute to ASD risk. In this study, we gathered a list of 46 NMD factors and regulators and investigated the role of genetic variants in these genes in ASD. By conducting a comprehensive search for Single Nucleotide Variants (SNVs) in NMD genes using Whole Exome Sequencing data from 1828 ASD patients, we identified 270 SNVs predicted to be damaging in 28.7% of the population. We also analyzed Copy Number Variants (CNVs) from two cohorts of ASD patients (N = 3570) and discovered 38 CNVs in 1% of cases. Importantly, we discovered 136 genetic variants (125 SNVs and 11 CNVs) in 258 ASD patients that were located within protein domains required for NMD. These gene variants are classified as damaging using in silico prediction tools, and therefore may interfere with proper NMD function in ASD. The discovery of NMD genes as candidates for ASD in large patient genomic datasets provides evidence supporting the involvement of the NMD pathway in ASD pathophysiology.

Three decades of ASD genetics: building a foundation for neurobiological understanding and treatment

Methodological advances over the last three decades have led to a profound transformation in our understanding of the genetic origins of neuropsychiatric disorders. This is exemplified by the study of autism spectrum disorders (ASDs) for which microarrays, whole exome sequencing and whole genome sequencing have yielded over a hundred causal loci. Genome-wide association studies in ASD have also been fruitful, identifying 5 genome-wide significant loci thus far and demonstrating a substantial role for polygenic inherited risk. Approaches rooted in systems biology and functional genomics have increasingly placed genes implicated by risk variants into biological context. Genetic risk affects a finite group of cell-types and biological processes, converging primarily on early stages of brain development (though, the expression of many risk genes persists through childhood). Coupled with advances in stem cell-based human in vitro model systems, these findings provide a basis for developing mechanistic models of disease pathophysiology.

Dysregulation of Multiple Signaling Neurodevelopmental Pathways during Embryogenesis: A Possible Cause of Autism Spectrum Disorder

Understanding the autistic brain and the involvement of genetic, non-genetic, and numerous signaling pathways in the etiology and pathophysiology of autism spectrum disorder (ASD) is complex, as is evident from various studies. Apart from multiple developmental disorders of the brain, autistic subjects show a few characteristics like impairment in social communications related to repetitive, restricted, or stereotypical behavior, which suggests alterations in neuronal circuits caused by defects in various signaling pathways during embryogenesis. Most of the research studies on ASD subjects and genetic models revealed the involvement of mutated genes with alterations of numerous signaling pathways like Wnt, hedgehog, and Retinoic Acid (RA). Despite significant improvement in understanding the pathogenesis and etiology of ASD, there is an increasing awareness related to it as well as a need for more in-depth research because no effective therapy has been developed to address ASD symptoms. Therefore, identifying better therapeutic interventions like "novel drugs for ASD" and biomarkers for early detection and disease condition determination are required. This review article investigated various etiological factors as well as the signaling mechanisms and their alterations to understand ASD pathophysiology. It summarizes the mechanism of signaling pathways, their significance, and implications for ASD.

Targeted sequencing and integrative analysis to prioritize candidate genes in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) are a group of diseases characterized by high heterogeneity and frequently co-occurring symptoms. The mutational spectrum in patients with NDDs is largely incomplete. Here, we sequenced 547 genes from 1102 patients with NDDs and validated 1271 potential functional variants, including 108 de novo variants (DNVs) in 78 autosomal genes and seven inherited hemizygous variants in six X chromosomal genes. Notably, 36 of these 78 genes are the first to be reported in Chinese patients with NDDs. By integrating our genetic data with public data, we prioritized 212 NDD candidate genes with FDR < 0.1, including 17 novel genes. The novel candidate genes interacted or were co-expressed with known candidate genes, forming a functional network involved in known pathways. We highlighted MSL2, which carried two de novo protein-truncating variants (p.L192Vfs*3 and p.S486Ifs*11) and was frequently connected with known candidate genes. This study provides the mutational spectrum of NDDs in China and prioritizes 212 NDD candidate genes for further functional validation and genetic counseling.

Targeted sequencing and integrative analysis of 3,195 Chinese patients with neurodevelopmental disorders prioritized 26 novel candidate genes

Neurodevelopmental disorders (NDDs) are a set of complex disorders characterized by diverse and co-occurring clinical symptoms. The genetic contribution in patients with NDDs remains largely unknown. Here, we sequence 519 NDD-related genes in 3,195 Chinese probands with neurodevelopmental phenotypes and identify 2,522 putative functional mutations consisting of 137 de novo mutations (DNMs) in 86 genes and 2,385 rare inherited mutations (RIMs) with 22 X-linked hemizygotes in 13 genes, 2 homozygous mutations in 2 genes and 23 compound heterozygous mutations in 10 genes. Furthermore, the DNMs of 16,807 probands with NDDs are retrieved from public datasets and combine in an integrated analysis with the mutation data of our Chinese NDD probands by taking 3,582 in-house controls of Chinese origin as background. We prioritize 26 novel candidate genes. Notably, six of these genes - ITSN1, UBR3, CADM1, RYR3, FLNA, and PLXNA3 - preferably contribute to autism spectrum disorders (ASDs), as demonstrated by high co-expression and/or interaction with ASD genes confirmed via rescue experiments in a mouse model. Importantly, these genes are differentially expressed in the ASD cortex in a significant manner and involved in ASD-associated networks. Together, our study expands the genetic spectrum of Chinese NDDs, further facilitating both basic and translational research.

Rare-variant collapsing analyses for complex traits: guidelines and applications

The first phase of genome-wide association studies (GWAS) assessed the role of common variation in human disease. Advances optimizing and economizing high-throughput sequencing have enabled a second phase of association studies that assess the contribution of rare variation to complex disease in all protein-coding genes. Unlike the early microarray-based studies, sequencing-based studies catalogue the full range of genetic variation, including the evolutionarily youngest forms. Although the experience with common variants helped establish relevant standards for genome-wide studies, the analysis of rare variation introduces several challenges that require novel analysis approaches.

Comprehensive Analysis of Rare Variants of 101 Autism-Linked Genes in a Hungarian Cohort of Autism Spectrum Disorder Patients

Background

Autism spectrum disorder (ASD) is genetically and phenotypically heterogeneous. Former genetic studies suggested that both common and rare genetic variants play a role in the etiology. In this study, we aimed to analyze rare variants detected by next generation sequencing (NGS) in an autism cohort from Hungary.

Methods

We investigated the yield of NGS panel sequencing of an unselected ASD cohort (N = 174 ) for the detection of ASD associated syndromes. Besides, we analyzed rare variants in a common disease-rare variant framework and performed rare variant burden analysis and gene enrichment analysis in phenotype based clusters.

Results

We have diagnosed 13 molecularly proven syndromic autism cases. Strongest indicators of syndromic autism were intellectual disability, epilepsy or other neurological plus symptoms. Rare variant analysis on a cohort level confirmed the association of five genes with autism (AUTS2, NHS, NSD1, SLC9A9, and VPS13). We found no correlation between rare variant burden and number of minor malformation or autism severity. We identified four phenotypic clusters, but no specific gene was enriched in a given cluster.

Conclusion

Our study indicates that NGS panel gene sequencing can be useful, where the clinical picture suggests a clinically defined syndromic autism. In this group, targeted panel sequencing may provide reasonable diagnostic yield. Unselected NGS panel screening in the clinic remains controversial, because of uncertain utility, and difficulties of the variant interpretation. However, the detected rare variants may still significantly influence autism risk and subphenotypes in a polygenic model, but to detect the effects of these variants larger cohorts are needed.

Targeted resequencing of 358 candidate genes for autism spectrum disorder in a Chinese cohort reveals diagnostic potential and genotype-phenotype correlations

Autism spectrum disorder (ASD) is a childhood neuropsychiatric disorder with a complex genetic architecture. The diagnostic potential of a targeted panel of ASD genes has only been evaluated in small cohorts to date and is especially understudied in the Chinese population. Here, we designed a capture panel with 358 genes (111 syndromic and 247 nonsyndromic) for ASD and sequenced a Chinese cohort of 539 cases evaluated with the Autism Diagnostic Interview‐Revised (ADI‐R) and the Autism Diagnostic Observation Schedule (ADOS) as well as 512 controls. ASD cases were found to carry significantly more ultra‐rare functional variants than controls. A subset of 78 syndromic and 54 nonsyndromic genes was the most significantly associated and should be given high priority in the future screening of ASD patients. Pathogenic and likely pathogenic variants were detected in 9.5% of cases. Variants in SHANK3 and SHANK2 were the most frequent, especially in females, and occurred in 1.2% of cases. Duplications of 15q11–13 were detected in 0.8% of cases. Variants in CNTNAP2 and MEF2C were correlated with epilepsy/tics in cases. Our findings reveal the diagnostic potential of ASD genetic panel testing and new insights regarding the variant spectrum. Genotype–phenotype correlations may facilitate the diagnosis and management of ASD.

Rare variant association test in family-based sequencing studies

The objective of this article is to introduce valid and robust methods for the analysis of rare variants for family-based exome chips, whole-exome sequencing or whole-genome sequencing data. Family-based designs provide unique opportunities to detect genetic variants that complement studies of unrelated individuals. Currently, limited methods and software tools have been developed to assist family-based association studies with rare variants, especially for analyzing binary traits. In this article, we address this gap by extending existing burden and kernel-based gene set association tests for population data to related samples, with a particular emphasis on binary phenotypes. The proposed approach blends the strengths of kernel machine methods and generalized estimating equations. Importantly, the efficient generalized kernel score test can be applied as a mega-analysis framework to combine studies with different designs. We illustrate the application of the proposed method using data from an exome sequencing study of autism. Methods discussed in this article are implemented in an R package 'gskat', which is available on CRAN and GitHub.

XPAT: a toolkit to conduct cross-platform association studies with heterogeneous sequencing datasets

High-throughput sequencing data are increasingly being made available to the research community for secondary analyses, providing new opportunities for large-scale association studies. However, heterogeneity in target capture and sequencing technologies often introduce strong technological stratification biases that overwhelm subtle signals of association in studies of complex traits. Here, we introduce the Cross-Platform Association Toolkit, XPAT, which provides a suite of tools designed to support and conduct large-scale association studies with heterogeneous sequencing datasets. XPAT includes tools to support cross-platform aware variant calling, quality control filtering, gene-based association testing and rare variant effect size estimation. To evaluate the performance of XPAT, we conducted case-control association studies for three diseases, including 783 breast cancer cases, 272 ovarian cancer cases, 205 Crohn disease cases and 3507 shared controls (including 1722 females) using sequencing data from multiple sources. XPAT greatly reduced Type I error inflation in the case-control analyses, while replicating many previously identified disease-gene associations. We also show that association tests conducted with XPAT using cross-platform data have comparable performance to tests using matched platform data. XPAT enables new association studies that combine existing sequencing datasets to identify genetic loci associated with common diseases and other complex traits.

A Powerful Gene-Based Test Accommodating Common and Low-Frequency Variants to Detect Both Main Effects and Gene-Gene Interaction Effects in Case-Control Studies

Next-generation sequencing (NGS) has been widely used in genetic association studies to identify both common and rare variants associated with complex diseases. Various statistical association tests have been developed to analyze NGS data; however, most focus on identifying the marginal effects of a set of genetic variants on the disease. Only a few association tests for NGS data analysis have considered the interaction effects between genes. We developed three powerful gene-based gene-gene interaction tests for testing both the main effects and the interaction effects of common, low-frequency, and common with low-frequency variant pairs between two genes (the IGOF tests) in case-control studies using NGS data. We performed a comprehensive simulation study to verify that the proposed tests had appropriate type I error rates and significantly higher power than did other interaction tests for analyzing NGS data. The tests were applied to a whole-exome sequencing dataset for autism spectrum disorder (ASD) and the significant results were evaluated in another independent ASD cohort. The IGOF tests were implemented in C++ and are available at http://igof.sourceforge.net.

Integrated rare variant-based risk gene prioritization in disease case-control sequencing studies

Rare variants of major effect play an important role in human complex diseases and can be discovered by sequencing-based genome-wide association studies. Here, we introduce an integrated approach that combines the rare variant association test with gene network and phenotype information to identify risk genes implicated by rare variants for human complex diseases. Our data integration method follows a 'discovery-driven' strategy without relying on prior knowledge about the disease and thus maintains the unbiased character of genome-wide association studies. Simulations reveal that our method can outperform a widely-used rare variant association test method by 2 to 3 times. In a case study of a small disease cohort, we uncovered putative risk genes and the corresponding rare variants that may act as genetic modifiers of congenital heart disease in 22q11.2 deletion syndrome patients. These variants were missed by a conventional approach that relied on the rare variant association test alone.

Case-control sequencing studies are a promising design to uncover risk genes of human complex diseases implicated by rare variants. The recent development of different types of rare variant association tests has improved the statistical power to identify disease genes that harbor risk rare variants. However, none of the recent sequencing-based genome-wide association studies identified robust disease association of rare variants or genes based on them. Due to limited sample sizes that can be feasibly achieved in real applications, current rare variant association tests can only generate marginal association signals for most risk genes. Here we proposed an integrated method that combined association signals with orthogonal biological evidence to uncover risk genes in sequencing studies. Designed to address the lack-of-power issue, our method was shown to effectively uncover risk genes with marginal association signals in data simulation. Indeed, in a real application demonstrated in our case study our method disclosed important risk genes of congenital heart disease in 22q11.2 deletion syndrome that were missed by the previous study.

On the substructure controls in rare variant analysis: Principal components or variance components?

Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e.g., common, less frequent, rare, or all variants) should be used to effectively control for confounding effects. We found that (i) PC-based methods can account for confounding effects in most scenarios except for admixture, although the number of sufficient PCs depends on the PS complexity and the type of variants used. (ii) PCs based on all variants (i.e., common + less frequent + rare) tend to require equal or fewer sufficient PCs and often achieve higher power than PCs based on other variant types. (iii) VC-based methods can effectively adjust for confounding in all scenarios (even for admixture), though the type of variants should be used to construct VC may vary. (iv) VC based on all variants works consistently in all scenarios, though its power may be sometimes lower than VC based on other variant types. Given that the best-performed method and which variants to use depend on the underlying unknown confounding mechanisms, a robust strategy is to perform SKAT analyses using VC-based methods based on all variants.

Four Genome-Wide Association Studies Identify New Extreme Longevity Variants

The search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators. To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort. We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years). The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease. The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span.

Targeted sequencing and functional analysis reveal brain-size-related genes and their networks in autism spectrum disorders

Autism spectrum disorder (ASD) represents a set of complex neurodevelopmental disorders with large degrees of heritability and heterogeneity. We sequenced 136 microcephaly or macrocephaly (Mic-Mac)-related genes and 158 possible ASD-risk genes in 536 Chinese ASD probands and detected 22 damaging de novo mutations (DNMs) in 20 genes, including CHD8 and SCN2A, with recurrent events. Nine of the 20 genes were previously reported to harbor DNMs in ASD patients from other populations, while 11 of them were first identified in present study. We combined genetic variations of the 294 sequenced genes from publicly available whole-exome or whole-genome sequencing studies (4167 probands plus 1786 controls) with our Chinese population (536 cases plus 1457 controls) to optimize the power of candidate-gene prioritization. As a result, we prioritized 67 ASD-candidate genes that exhibited significantly higher probabilities of haploinsufficiency and genic intolerance, and significantly interacted and co-expressed with each another, as well as other known ASD-risk genes. Probands with DNMs or rare inherited mutations in the 67 candidate genes exhibited significantly lower intelligence quotients, supporting their strong functional impact. In addition, we prioritized 39 ASD-related Mic-Mac-risk genes, and showed their interaction and co-expression in a functional network that converged on chromatin remodeling, synapse transmission and cell cycle progression. Genes within the three functional subnetworks exhibited distinct and recognizable spatiotemporal-expression patterns in human brains and laminar-expression profiles in the developing neocortex, highlighting their important roles in brain development. Our results indicate some of Mic-Mac-risk genes are involved in ASD.

Genomic Characteristics of Gender Dysphoria Patients and Identification of Rare Mutations in RYR3 Gene

Gender dysphoria (GD) is characterized by an incongruence between the gender assigned at birth and the gender with which one identifies. The biological mechanisms of GD are unclear. While common genetic variants are associated with GD, positive findings have not always been replicated. To explore the role of rare variants in GD susceptibility within the Han Chinese population, whole-genome sequencing of 9 Han female-to-male transsexuals (FtMs) and whole-exome sequencing of 4 Han male-to-female transsexuals (MtFs) were analyzed using a pathway burden analysis in which variants are first collapsed at the gene level and then by Gene Ontology terms. Novel nonsynonymous variants in ion transport genes were significantly enriched in FtMs (P- value, 2.41E-10; Fold enrichment, 2.8) and MtFs (P- value, 1.04E-04; Fold enrichment, 2.3). Gene burden analysis comparing 13 GD cases and 100 controls implicated RYR3, with three heterozygous damaging mutations in unrelated FtMs and zero in controls (P = 0.001). Importantly, protein structure modeling of the RYR3 mutations indicated that the R1518H mutation made a large structural change in the RYR3 protein. Overall, our results provide information about the genetic basis of GD.

Bio-collections in autism research

Autism spectrum disorder (ASD) is a group of complex neurodevelopmental disorders with diverse clinical manifestations and symptoms. In the last 10 years, there have been significant advances in understanding the genetic basis for ASD, critically supported through the establishment of ASD bio-collections and application in research. Here, we summarise a selection of major ASD bio-collections and their associated findings. Collectively, these include mapping ASD candidate genes, assessing the nature and frequency of gene mutations and their association with ASD clinical subgroups, insights into related molecular pathways such as the synapses, chromatin remodelling, transcription and ASD-related brain regions. We also briefly review emerging studies on the use of induced pluripotent stem cells (iPSCs) to potentially model ASD in culture. These provide deeper insight into ASD progression during development and could generate human cell models for drug screening. Finally, we provide perspectives concerning the utilities of ASD bio-collections and limitations, and highlight considerations in setting up a new bio-collection for ASD research.

Vitamin D-related genes are subjected to significant de novo mutation burdens in autism spectrum disorder

Vitamin D deficiency is a putative environmental risk factor for autism spectrum disorder (ASD). Besides, de novo mutations (DNMs) play essential roles in ASD. However, it remains unclear whether vitamin D-related genes (VDRGs) carry a strong DNM burden. For the 943 reported VDRGs, we analyzed publicly-available DNMs from 4,327 ASD probands and 3,191 controls. We identified 126 and 44 loss-of-function or deleterious missense mutations in the probands and the controls, respectively, representing a significantly higher DNM burden (p = 1.06 × 10^-5 ; odds ratio = 2.11). Specifically, 18 of the VDRGs were found to harbor recurrent functional DNMs in the probands, compared with only one in the controls. In addition, we found that 108 VDRGs with functional DNMs in the probands were significantly more likely to exhibit haploinsufficiency and genic intolerance (p < 0.0078). These VDRGs were also significantly interconnected and co-expressed, and also with other known ASD-risk genes (p < 0.0014), thereby forming a functional network enriched in chromatin modification, transcriptional regulation, and neuronal function. We provide straightforward genetic evidences for the first time that VDRGs with a strong degree of DNM burden in ASD and DNMs of VDRGs could be involved in the mechanism underlying in ASD pathogenesis.

Anatomy and Cell Biology of Autism Spectrum Disorder: Lessons from Human Genetics

Until recently autism spectrum disorder (ASD) was regarded as a neurodevelopmental condition with unknown causes and pathogenesis. In the footsteps of the revolution of genome technologies and genetics, and with its high degree of heritability, ASD became the first neuropsychiatric disorder for which clues towards molecular and cellular pathogenesis were uncovered by genetic identification of susceptibility genes. Currently several hundreds of risk genes have been assigned, with a recurrence below 1% in the ASD population. The multitude and diversity of known ASD genes has extended the clinical notion that ASD comprises very heterogeneous conditions ranging from severe intellectual disabilities to mild high-functioning forms. The results of genetics have allowed to pinpoint a limited number of cellular and molecular processes likely involved in ASD including protein synthesis, signal transduction, transcription/chromatin remodelling and synaptic function all playing an essential role in the regulation of synaptic homeostasis during brain development. In this context, we highlight the role of protein synthesis as a key process in ASD pathogenesis as it might be central in synaptic deregulation and a potential target for intervention. These current insights should lead to a rational design of interventions in molecular and cellular pathways of ASD pathogenesis that may be applied to affected individuals in the future.

From Common to Rare Variants: The Genetic Component of Alzheimer Disease

Alzheimer disease (AD) is a remarkable example of genetic heterogeneity. Extremely rare variants in the APP, PSEN1, or PSEN2 genes, or duplications of the APP gene cause autosomal dominant forms, generally with complete penetrance by the age of 65 years. Nonautosomal dominant forms are considered as a complex disorder with a high genetic component, whatever the age of onset. Although genetically heterogeneous, AD is defined by the same neuropathological criteria in all configurations. According to the amyloid cascade hypothesis, the Aβ peptide, which aggregates in AD brains, is a key player. APP, PSEN1, or PSEN2 gene mutations increase the production of more aggregation-prone forms of the Aβ peptide, triggering the pathological process. Several risk factors identified in association studies hit genes involved in Aβ production/secretion, aggregation, clearance, or toxicity. Among them, the APOE ε4 allele is a rare example of a common allele with a large effect size, the ORs ranging from 4 to 11-14 for heterozygous and homozygous carriers, respectively. In addition, genome-wide association studies have identified more than two dozen loci with a weak but significant association, the OR of the at-risk allele ranging from 1.08 to 1.30. Recently, the use of massive parallel sequencing has enabled the analysis of rare variants in a genome-wide manner. Two rare variants have been nominally associated with AD risk or protection (TREM2 p.R47H, MAF approximately 0.002, OR approximately 4 and APP p.A673T, MAF approximately 0.0005, OR approximately 0.2). Association analyses at the gene level identified rare loss-of-function and missense, predicted damaging, variants (MAF <0.01) in the SORL1 and ABCA7 genes associated with a moderate relative risk (OR approximately 5 and approximately 2.8, respectively). Although the latter analyses revealed association signals with moderately rare variants by collapsing them, the power to detect genes hit by extremely rare variants is still limited. An alternative approach is to consider the de novo paradigm, stating that de novo variants may contribute to AD genetics in sporadic patients. Here, we critically review AD genetics reports with a special focus on rare variants.

An exome-wide analysis of low frequency and rare variants in relation to risk of breast cancer in African American Women: the AMBER Consortium

A large percentage of breast cancer heritability remains unaccounted for, and most of the known susceptibility loci have been established in European and Asian populations. Rare variants may contribute to the unexplained heritability of this disease, including in women of African ancestry (AA). We conducted an exome-wide analysis of rare variants in relation to risk of overall and subtype-specific breast cancer in the African American Breast Cancer Epidemiology and Risk (AMBER) Consortium, which includes data from four large studies of AA women. Genotyping on the Illumina Human Exome Beadchip yielded data for 170 812 SNPs and 8287 subjects: 3629 cases (1093 estrogen receptor negative (ER-), 1968 ER+, 568 ER unknown) and 4658 controls, the largest exome chip study to date for AA breast cancer. Pooled gene-based association analyses were performed using the unified optimal sequence kernel association test (SKAT-O) for variants with minor allele frequency (MAF) ≤ 5%. In addition, each variant with MAF >0.5% was tested for association using logistic regression. There were no significant associations with overall breast cancer. However, a novel gene, FBXL22 (P = 8.2×10(-6)), and a gene previously identified in GWAS of European ancestry populations, PDE4D (P = 1.2×10(-6)), were significantly associated with ER- breast cancer after correction for multiple testing. Cases with the associated rare variants were also negative for progesterone and human epidermal growth factor receptors-thus, triple-negative cancer. Replication is required to confirm these gene-level associations, which are based on very small counts at extremely rare SNPs.

SORL1 rare variants: a major risk factor for familial early-onset Alzheimer's disease

The SORL1 protein plays a protective role against the secretion of the amyloid β peptide, a key event in the pathogeny of Alzheimer's disease. We assessed the impact of SORL1 rare variants in early-onset Alzheimer's disease (EOAD) in a case-control setting. We conducted a whole exome analysis among 484 French EOAD patients and 498 ethnically matched controls. After collapsing rare variants (minor allele frequency ≤1%), we detected an enrichment of disruptive and predicted damaging missense SORL1 variants in cases (odds radio (OR)=5.03, 95% confidence interval (CI)=(2.02-14.99), P=7.49.10(-5)). This enrichment was even stronger when restricting the analysis to the 205 cases with a positive family history (OR=8.86, 95% CI=(3.35-27.31), P=3.82.10(-7)). We conclude that predicted damaging rare SORL1 variants are a strong risk factor for EOAD and that the association signal is mainly driven by cases with positive family history.

GWASeq: targeted re-sequencing follow up to GWAS

BACKGROUND

For the last decade the conceptual framework of the Genome-Wide Association Study (GWAS) has dominated the investigation of human disease and other complex traits. While GWAS have been successful in identifying a large number of variants associated with various phenotypes, the overall amount of heritability explained by these variants remains small. This raises the question of how best to follow up on a GWAS, localize causal variants accounting for GWAS hits, and as a consequence explain more of the so-called "missing" heritability. Advances in high throughput sequencing technologies now allow for the efficient and cost-effective collection of vast amounts of fine-scale genomic data to complement GWAS.

RESULTS

We investigate these issues using a colon cancer dataset. After QC, our data consisted of 1993 cases, 899 controls. Using marginal tests of associations, we identify 10 variants distributed among six targeted regions that are significantly associated with colorectal cancer, with eight of the variants being novel to this study. Additionally, we perform so-called 'SNP-set' tests of association and identify two sets of variants that implicate both common and rare variants in the etiology of colorectal cancer.

CONCLUSIONS

Here we present a large-scale targeted re-sequencing resource focusing on genomic regions implicated in colorectal cancer susceptibility previously identified in several GWAS, which aims to 1) provide fine-scale targeted sequencing data for fine-mapping and 2) provide data resources to address methodological questions regarding the design of sequencing-based follow-up studies to GWAS. Additionally, we show that this strategy successfully identifies novel variants associated with colorectal cancer susceptibility and can implicate both common and rare variants.

Genes with de novo mutations are shared by four neuropsychiatric disorders discovered from NPdenovo database

Currently, many studies on neuropsychiatric disorders have utilized massive trio-based whole-exome sequencing (WES) and whole-genome sequencing (WGS) to identify numerous de novo mutations (DNMs). Here, we retrieved 17,104 DNMs from 3555 trios across four neuropsychiatric disorders: autism spectrum disorder, epileptic encephalopathy, intellectual disability and schizophrenia, in addition to unaffected siblings (control), from 36 studies by WES/WGS. After eliminating non-exonic variants, we focused on 3334 exonic DNMs for evaluation of their association with these diseases. Our results revealed a higher prevalence of DNMs in the probands of all four disorders compared with the one in the controls (P<1.3 × 10(-7)). The elevated DNM frequency is dominated by loss-of-function/deleterious single-nucleotide variants and frameshift indels (that is, extreme mutations, P<4.5 × 10(-5)). With extensive annotation of these 'extreme' mutations, we prioritized 764 candidate genes in these four disorders. A combined analysis of Gene Ontology, microRNA targets and transcription factor targets revealed shared biological process and non-coding regulatory elements of candidate genes in the pathology of neuropsychiatric disorders. In addition, weighted gene co-expression network analysis of human laminar-specific neocortical expression data showed that candidate genes are convergent on eight shared modules with specific layer enrichment and biological process features. Furthermore, we identified that 53 candidate genes are associated with more than one disorder (P<0.000001), suggesting a possibly shared genetic etiology underlying these disorders. Particularly, DNMs of the SCN2A gene are frequently occurred across all four disorders. Finally, we constructed a freely available NPdenovo database, which provides a comprehensive catalog of the DNMs identified in neuropsychiatric disorders.

Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci

Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1).

NETWORK ASSISTED ANALYSIS TO REVEAL THE GENETIC BASIS OF AUTISM

While studies show that autism is highly heritable, the nature of the genetic basis of this disorder remains illusive. Based on the idea that highly correlated genes are functionally interrelated and more likely to affect risk, we develop a novel statistical tool to find more potentially autism risk genes by combining the genetic association scores with gene co-expression in specific brain regions and periods of development. The gene dependence network is estimated using a novel partial neighborhood selection (PNS) algorithm, where node specific properties are incorporated into network estimation for improved statistical and computational efficiency. Then we adopt a hidden Markov random field (HMRF) model to combine the estimated network and the genetic association scores in a systematic manner. The proposed modeling framework can be naturally extended to incorporate additional structural information concerning the dependence between genes. Using currently available genetic association data from whole exome sequencing studies and brain gene expression levels, the proposed algorithm successfully identified 333 genes that plausibly affect autism risk.

Identification of Human Neuronal Protein Complexes Reveals Biochemical Activities and Convergent Mechanisms of Action in Autism Spectrum Disorders

The prevalence of autism spectrum disorders (ASDs) is rapidly growing, yet its molecular basis is poorly understood. We used a systems approach in which ASD candidate genes were mapped onto the ubiquitous human protein complexes and the resulting complexes were characterized. The studies revealed the role of histone deacetylases (HDAC1/2) in regulating the expression of ASD orthologs in the embryonic mouse brain. Proteome-wide screens for the co-complexed subunits with HDAC1 and six other key ASD proteins in neuronal cells revealed a protein interaction network, which displayed preferential expression in fetal brain development, exhibited increased deleterious mutations in ASD cases, and were strongly regulated by FMRP and MECP2 causal for Fragile X and Rett syndromes, respectively. Overall, our study reveals molecular components in ASD, suggests a shared mechanism between the syndromic and idiopathic forms of ASDs, and provides a systems framework for analyzing complex human diseases.

Targeted Next-Generation Sequencing Analysis of 1,000 Individuals with Intellectual Disability

To identify genetic causes of intellectual disability (ID), we screened a cohort of 986 individuals with moderate to severe ID for variants in 565 known or candidate ID‐associated genes using targeted next‐generation sequencing. Likely pathogenic rare variants were found in ∼11% of the cases (113 variants in 107/986 individuals: ∼8% of the individuals had a likely pathogenic loss‐of‐function [LoF] variant, whereas ∼3% had a known pathogenic missense variant). Variants in SETD5, ATRX, CUL4B, MECP2, and ARID1B were the most common causes of ID. This study assessed the value of sequencing a cohort of probands to provide a molecular diagnosis of ID, without the availability of DNA from both parents for de novo sequence analysis. This modeling is clinically relevant as 28% of all UK families with dependent children are single parent households. In conclusion, to diagnose patients with ID in the absence of parental DNA, we recommend investigation of all LoF variants in known genes that cause ID and assessment of a limited list of proven pathogenic missense variants in these genes. This will provide 11% additional diagnostic yield beyond the 10%–15% yield from array CGH alone.

Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data

Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees. Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms. Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent (IBD) sharing along the genome. In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data, and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees. Through simulations and application to real data, we showed that IBD can be reliably estimated across the genome, even at very low coverage (e.g. 2X), and genotype accuracy can be dramatically improved. Moreover, the improvement is more pronounced for variants with low frequencies, especially at low to intermediate coverage (e.g. 10X to 20X), making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees. We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing.

To identify disease variants that occur less frequently in population, sequencing families in which multiple individuals are affected is more powerful due to the enrichment of causal variants. An important step in such studies is to infer individual genotypes from sequencing data. Existing methods do not utilize full familial transmission information and therefore result in reduced accuracy of inferred genotypes. In this study we describe a new method that infers shared genetic materials among family members and then incorporate the shared genomic information in a novel algorithm that can accurately infer genotypes. Our method is particularly advantageous when inferring low frequency variants with fewer sequence data, making it effective in analyzing genome-wide sequence data. We implemented the algorithm in a computationally efficient tool to facilitate cost-effective sequencing in families for identifying disease genetic variants.

Association between Stress Response Genes and Features of Diurnal Cortisol Curves in the Multi-Ethnic Study of Atherosclerosis: A New Multi-Phenotype Approach for Gene-Based Association Tests

The hormone cortisol is likely to be a key mediator of the stress response that influences multiple physiologic systems that are involved in common chronic disease, including the cardiovascular system, the immune system, and metabolism. In this paper, a candidate gene approach was used to investigate genetic contributions to variability in multiple correlated features of the daily cortisol profile in a sample of European Americans, African Americans, and Hispanic Americans from the Multi-Ethnic Study of Atherosclerosis (MESA). We proposed and applied a new gene-level multiple-phenotype analysis and carried out a meta-analysis to combine the ethnicity specific results. This new analysis, instead of a more routine single marker-single phenotype approach identified a significant association between one gene (ADRB2) and cortisol features (meta-analysis p-value=0.0025), which was not identified by three other commonly used existing analytic strategies: 1. Single marker association tests involving each single cortisol feature separately; 2. Single marker association tests jointly testing for multiple cortisol features; 3. Gene-level association tests separately carried out for each single cortisol feature. The analytic strategies presented consider different hypotheses regarding genotype-phenotype association and imply different costs of multiple testing. The proposed gene-level analysis integrating multiple cortisol features across multiple ethnic groups provides new insights into the gene-cortisol association.

A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing

Results from numerous linkage and association studies have greatly deepened scientists’ understanding of the genetic basis of many human diseases, yet some important questions remain unanswered. For example, although a large number of disease-associated loci have been identified from genome-wide association studies in the past 10 years, it is challenging to interpret these results as most disease-associated markers have no clear functional roles in disease etiology, and all the identified genomic factors only explain a small portion of disease heritability. With the help of next-generation sequencing (NGS), diverse types of genomic and epigenetic variations can be detected with high accuracy. More importantly, instead of using linkage disequilibrium to detect association signals based on a set of pre-set probes, NGS allows researchers to directly study all the variants in each individual, therefore promises opportunities for identifying functional variants and a more comprehensive dissection of disease heritability. Although the current scale of NGS studies is still limited due to the high cost, the success of several recent studies suggests the great potential for applying NGS in genomic epidemiology, especially as the cost of sequencing continues to drop. In this review, we discuss several pioneer applications of NGS, summarize scientific discoveries for rare and complex diseases, and compare various study designs including targeted sequencing and whole-genome sequencing using population-based and family-based cohorts. Finally, we highlight recent advancements in statistical methods proposed for sequencing analysis, including group-based association tests, meta-analysis techniques, and annotation tools for variant prioritization.

Integrated analysis of whole-exome sequencing and transcriptome profiling in males with autism spectrum disorders

BACKGROUND

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders with high heritability. Recent findings support a highly heterogeneous and complex genetic etiology including rare de novo and inherited mutations or chromosomal rearrangements as well as double or multiple hits.

METHODS

We performed whole-exome sequencing (WES) and blood cell transcriptome by RNAseq in a subset of male patients with idiopathic ASD (n = 36) in order to identify causative genes, transcriptomic alterations, and susceptibility variants.

RESULTS

We detected likely monogenic causes in seven cases: five de novo (SCN2A, MED13L, KCNV1, CUL3, and PTEN) and two inherited X-linked variants (MAOA and CDKL5). Transcriptomic analyses allowed the identification of intronic causative mutations missed by the usual filtering of WES and revealed functional consequences of some rare mutations. These included aberrant transcripts (PTEN, POLR3C), deregulated expression in 1.7% of mutated genes (that is, SEMA6B, MECP2, ANK3, CREBBP), allele-specific expression (FUS, MTOR, TAF1C), and non-sense-mediated decay (RIT1, ALG9). The analysis of rare inherited variants showed enrichment in relevant pathways such as the PI3K-Akt signaling and the axon guidance.

CONCLUSIONS

Integrative analysis of WES and blood RNAseq data has proven to be an efficient strategy to identify likely monogenic forms of ASD (19% in our cohort), as well as additional rare inherited mutations that can contribute to ASD risk in a multifactorial manner. Blood transcriptomic data, besides validating 88% of expressed variants, allowed the identification of missed intronic mutations and revealed functional correlations of genetic variants, including changes in splicing, expression levels, and allelic expression.

Genetics and Epigenetics of Eating Disorders

Eating disorders (EDs) are serious psychiatric conditions influenced by biological, psychological, and sociocultural factors. A better understanding of the genetics of these complex traits and the development of more sophisticated molecular biology tools have advanced our understanding of the etiology of EDs. The aim of this review is to critically evaluate the literature on the genetic research conducted on three major EDs: anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). We will first review the diagnostic criteria, clinical features, prevalence, and prognosis of AN, BN, and BED, followed by a review of family, twin, and adoption studies. We then review the history of genetic studies of EDs covering linkage analysis, candidate gene association studies, genome-wide association studies, and the study of rare variants in EDs. Our review also incorporates a translational perspective by covering animal models of ED-related phenotypes. Finally, we review the nascent field of epigenetics of EDs and a look forward to future directions for ED genetic research.

Meta-Analysis of Gene Expression in Autism Spectrum Disorder

Autism spectrum disorders (ASD) are clinically heterogeneous and biologically complex. In general it remains unclear, what biological factors lead to changes in the brains of autistic individuals. A considerable number of transcriptome analyses have been performed in attempts to address this question, but their findings lack a clear consensus. As a result, each of these individual studies has not led to any significant advance in understanding the autistic phenotype as a whole. Here, we report a meta-analysis of more than 1000 microarrays across twelve independent studies on expression changes in ASD compared to unaffected individuals, in both blood and brain tissues. We identified a number of known and novel genes that are consistently differentially expressed across three studies of the brain (71 samples in total). A subset of the highly ranked genes is suggestive of effects on mitochondrial function. In blood, consistent changes were more difficult to identify, despite individual studies tending to exhibit larger effects than the brain studies. Our results are the strongest evidence to date of a common transcriptome signature in the brains of individuals with ASD.

Rare autism-associated variants implicate syntaxin 1 (STX1 R26Q) phosphorylation and the dopamine transporter (hDAT R51W) in dopamine neurotransmission and behaviors

Background

Syntaxin 1 (STX1) is a presynaptic plasma membrane protein that coordinates synaptic vesicle fusion. STX1 also regulates the function of neurotransmitter transporters, including the dopamine (DA) transporter (DAT). The DAT is a membrane protein that controls DA homeostasis through the high-affinity re-uptake of synaptically released DA.

Methods

We adopt newly developed animal models and state-of-the-art biophysical techniques to determine the contribution of the identified gene variants to impairments in DA neurotransmission observed in autism spectrum disorder (ASD).

Outcomes

Here, we characterize two independent autism-associated variants in the genes that encode STX1 and the DAT. We demonstrate that each variant dramatically alters DAT function. We identify molecular mechanisms that converge to inhibit reverse transport of DA and DA-associated behaviors. These mechanisms involve decreased phosphorylation of STX1 at Ser14 mediated by casein kinase 2 as well as a reduction in STX1/DAT interaction. These findings point to STX1/DAT interactions and STX1 phosphorylation as key regulators of DA homeostasis.

Interpretation

We determine the molecular identity and the impact of these variants with the intent of defining DA dysfunction and associated behaviors as possible complications of ASD.

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We report two independent autism-associated variants in syntaxin and the dopamine transporter.

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The variants in syntaxin and dopamine transporter each impair reverse transport of dopamine.

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Dysregulation of dopamine neurotransmission may represent a complication of autism spectrum disorder.

No evidence for association of autism with rare heterozygous point mutations in Contactin-Associated Protein-Like 2 (CNTNAP2), or in Other Contactin-Associated Proteins or Contactins

Contactins and Contactin-Associated Proteins, and Contactin-Associated Protein-Like 2 (CNTNAP2) in particular, have been widely cited as autism risk genes based on findings from homozygosity mapping, molecular cytogenetics, copy number variation analyses, and both common and rare single nucleotide association studies. However, data specifically with regard to the contribution of heterozygous single nucleotide variants (SNVs) have been inconsistent. In an effort to clarify the role of rare point mutations in CNTNAP2 and related gene families, we have conducted targeted next-generation sequencing and evaluated existing sequence data in cohorts totaling 2704 cases and 2747 controls. We find no evidence for statistically significant association of rare heterozygous mutations in any of the CNTN or CNTNAP genes, including CNTNAP2, placing marked limits on the scale of their plausible contribution to risk.

Prior genetic studies of autism spectrum disorders (ASD) have demonstrated a role for Contactin-Associated Protein-Like 2 protein (CNTNAP2), as well as for other genes that code for Contactin proteins and Contactin-Associated Proteins. While there is strong evidence that the loss of two copies of the gene CNTNAP2 causes autism and epilepsy, the impact of mutations in only one copy of this gene, or in only one copy of related genes, is less clear. We performed large-scale DNA sequencing on a cohort of over 1000 autism patients and nearly 1000 unaffected controls and did not find significant association at any of 6 genes in the Contactin family and 4 genes in the Contactin-Associated Protein family when looking for rare mutations that are predicted to be disruptive to the protein’s function and are present in only one copy of the respective gene. We then combined the data on CNTNAP2 from our laboratory with CNTNAP2 data from another research laboratory, and found no significant association of deleterious heterozygous mutations at this gene. Given the paucity of nonsense mutations identified across the combined sample, an assessment of their impact was circumscribed. However, missense heterozygous mutations in CNTNAP2 and in other Contactins or Contactin-Associated Proteins are not elevated in affected individuals versus controls and, consequently, do not have a marked impact, as a group, on the risk for autism spectrum disorders.

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.

Resequencing and association analysis of PTPRA, a possible susceptibility gene for schizophrenia and autism spectrum disorders

Background

The PTPRA gene, which encodes the protein RPTP-α, is critical to neurodevelopment. Previous linkage studies, genome-wide association studies, controlled expression analyses and animal models support an association with both schizophrenia and autism spectrum disorders, both of which share a substantial portion of genetic risks.

Methods

We sequenced the protein-encoding areas of the PTPRA gene for single nucleotide polymorphisms or small insertions/deletions (InDel) in 382 schizophrenia patients. To validate their association with the disorders, rare (minor allele frequency <1%), missense mutations as well as one InDel in the 3′UTR region were then genotyped in another independent sample set comprising 944 schizophrenia patients, 336 autism spectrum disorders patients, and 912 healthy controls.

Results

Eight rare mutations, including 3 novel variants, were identified during the mutation-screening phase. In the following association analysis, L59P, one of the two missense mutations, was only observed among patients of schizophrenia. Additionally, a novel duplication in the 3′UTR region, 174620_174623dupTGAT, was predicted to be located within a Musashi Binding Element.

Major Conclusions

No evidence was seen for the association of rare, missense mutations in the PTPRA gene with schizophrenia or autism spectrum disorders; however, we did find some rare variants with possibly damaging effects that may increase the susceptibility of carriers to the disorders.

Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations

Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.

SLC6A3 coding variant Ala559Val found in two autism probands alters dopamine transporter function and trafficking

Emerging evidence associates dysfunction in the dopamine (DA) transporter (DAT) with the pathophysiology of autism spectrum disorder (ASD). The human DAT (hDAT; SLC6A3) rare variant with an Ala to Val substitution at amino acid 559 (hDAT A559V) was previously reported in individuals with bipolar disorder or attention-deficit hyperactivity disorder (ADHD). We have demonstrated that this variant is hyper-phosphorylated at the amino (N)-terminal serine (Ser) residues and promotes an anomalous DA efflux phenotype. Here, we report the novel identification of hDAT A559V in two unrelated ASD subjects and provide the first mechanistic description of its impaired trafficking phenotype. DAT surface expression is dynamically regulated by DAT substrates including the psychostimulant amphetamine (AMPH), which causes hDAT trafficking away from the plasma membrane. The integrity of DAT trafficking directly impacts DA transport capacity and therefore dopaminergic neurotransmission. Here, we show that hDAT A559V is resistant to AMPH-induced cell surface redistribution. This unique trafficking phenotype is conferred by altered protein kinase C β (PKCβ) activity. Cells expressing hDAT A559V exhibit constitutively elevated PKCβ activity, inhibition of which restores the AMPH-induced hDAT A559V membrane redistribution. Mechanistically, we link the inability of hDAT A559V to traffic in response to AMPH to the phosphorylation of the five most distal DAT N-terminal Ser. Mutation of these N-terminal Ser to Ala restores AMPH-induced trafficking. Furthermore, hDAT A559V has a diminished ability to transport AMPH, and therefore lacks AMPH-induced DA efflux. Pharmacological inhibition of PKCβ or Ser to Ala substitution in the hDAT A559V background restores AMPH-induced DA efflux while promoting intracellular AMPH accumulation. Although hDAT A559V is a rare variant, it has been found in multiple probands with neuropsychiatric disorders associated with imbalances in DA neurotransmission, including ADHD, bipolar disorder, and now ASD. These findings provide valuable insight into a new cellular phenotype (altered hDAT trafficking) supporting dysregulated DA function in these disorders. They also provide a novel potential target (PKCβ) for therapeutic interventions in individuals with ASD.

Consensus Genotyper for Exome Sequencing (CGES): improving the quality of exome variant genotypes

MOTIVATION

The development of cost-effective next-generation sequencing methods has spurred the development of high-throughput bioinformatics tools for detection of sequence variation. With many disparate variant-calling algorithms available, investigators must ask, 'Which method is best for my data?' Machine learning research has shown that so-called ensemble methods that combine the output of multiple models can dramatically improve classifier performance. Here we describe a novel variant-calling approach based on an ensemble of variant-calling algorithms, which we term the Consensus Genotyper for Exome Sequencing (CGES). CGES uses a two-stage voting scheme among four algorithm implementations. While our ensemble method can accept variants generated by any variant-calling algorithm, we used GATK2.8, SAMtools, FreeBayes and Atlas-SNP2 in building CGES because of their performance, widespread adoption and diverse but complementary algorithms.

RESULTS

We apply CGES to 132 samples sequenced at the Hudson Alpha Institute for Biotechnology (HAIB, Huntsville, AL) using the Nimblegen Exome Capture and Illumina sequencing technology. Our sample set consisted of 40 complete trios, two families of four, one parent-child duo and two unrelated individuals. CGES yielded the fewest total variant calls (N(CGES) = 139° 897), the highest Ts/Tv ratio (3.02), the lowest Mendelian error rate across all genotypes (0.028%), the highest rediscovery rate from the Exome Variant Server (EVS; 89.3%) and 1000 Genomes (1KG; 84.1%) and the highest positive predictive value (PPV; 96.1%) for a random sample of previously validated de novo variants. We describe these and other quality control (QC) metrics from consensus data and explain how the CGES pipeline can be used to generate call sets of varying quality stringency, including consensus calls present across all four algorithms, calls that are consistent across any three out of four algorithms, calls that are consistent across any two out of four algorithms or a more liberal set of all calls made by any algorithm.

AVAILABILITY AND IMPLEMENTATION

To enable accessible, efficient and reproducible analysis, we implement CGES both as a stand-alone command line tool available for download in GitHub and as a set of Galaxy tools and workflows configured to execute on parallel computers.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rare-variant association analysis: study designs and statistical tests

Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions.

Statistical power and significance testing in large-scale genetic studies

Significance testing was developed as an objective method for summarizing statistical evidence for a hypothesis. It has been widely adopted in genetic studies, including genome-wide association studies and, more recently, exome sequencing studies. However, significance testing in both genome-wide and exome-wide studies must adopt stringent significance thresholds to allow multiple testing, and it is useful only when studies have adequate statistical power, which depends on the characteristics of the phenotype and the putative genetic variant, as well as the study design. Here, we review the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants.

The impact of population demography and selection on the genetic architecture of complex traits

Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequency, and effect sizes of mutations that contribute risk to complex traits. Because researchers are performing exome sequencing studies aimed at uncovering the role of low-frequency variants in the risk of complex traits, this topic is of critical importance. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant associations with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant associations detected. These findings suggest recent population history may be an important factor influencing the power of association tests and in accounting for the missing heritability of certain complex traits.

Large-scale genomics unveils the genetic architecture of psychiatric disorders

Family study results are consistent with genetic effects making substantial contributions to risk of psychiatric disorders such as schizophrenia, yet robust identification of specific genetic variants that explain variation in population risk had been disappointing until the advent of technologies that assay the entire genome in large samples. We highlight recent progress that has led to a better understanding of the number of risk variants in the population and the interaction of allele frequency and effect size. The emerging genetic architecture implies a large number of contributing loci (that is, a high genome-wide mutational target) and suggests that genetic risk of psychiatric disorders involves the combined effects of many common variants of small effect, as well as rare and de novo variants of large effect. The capture of a substantial proportion of genetic risk facilitates new study designs to investigate the combined effects of genes and the environment.

Kernel-machine testing coupled with a rank-truncation method for genetic pathway analysis

Traditional genome-wide association studies (GWASs) usually focus on single-marker analysis, which only accesses marginal effects. Pathway analysis, on the other hand, considers biological pathway gene marker hierarchical structure and therefore provides additional insights into the genetic architecture underlining complex diseases. Recently, a number of methods for pathway analysis have been proposed to assess the significance of a biological pathway from a collection of single-nucleotide polymorphisms. In this study, we propose a novel approach for pathway analysis that assesses the effects of genes using the sequence kernel association test and the effects of pathways using an extended adaptive rank truncated product statistic. It has been increasingly recognized that complex diseases are caused by both common and rare variants. We propose a new weighting scheme for genetic variants across the whole allelic frequency spectrum to be analyzed together without any form of frequency cutoff for defining rare variants. The proposed approach is flexible. It is applicable to both binary and continuous traits, and incorporating covariates is easy. Furthermore, it can be readily applied to GWAS data, exome-sequencing data, and deep resequencing data. We evaluate the new approach on data simulated under comprehensive scenarios and show that it has the highest power in most of the scenarios while maintaining the correct type I error rate. We also apply our proposed methodology to data from a study of the association between bipolar disorder and candidate pathways from Wellcome Trust Case Control Consortium (WTCCC) to show its utility.