Copy number variants (CNVs) have a major role in the etiology of autism spectrum disorders (ASD), and several of these have reached statistical significance in case-control analyses. Nevertheless, current ASD cohorts are not large enough to detect very rare CNVs that may be causative or contributory (that is, risk alleles). Here, we use a tiered approach, in which clinically significant CNVs are first identified in large clinical cohorts of neurodevelopmental disorders (including but not specific to ASD), after which these CNVs are then systematically identified within well-characterized ASD cohorts. We focused our initial analysis on 48 recurrent CNVs (segmental duplication-mediated 'hotspots') from 24 loci in 31 516 published clinical cases with neurodevelopmental disorders and 13 696 published controls, which yielded a total of 19 deletion CNVs and 11 duplication CNVs that reached statistical significance. We then investigated the overlap of these 30 CNVs in a combined sample of 3955 well-characterized ASD cases from three published studies. We identified 73 deleterious recurrent CNVs, including 36 deletions from 11 loci and 37 duplications from seven loci, for a frequency of 1 in 54; had we considered the ASD cohorts alone, only 58 CNVs from eight loci (24 deletions from three loci and 34 duplications from five loci) would have rea ched statistical significance. In conclusion, until there are sufficiently large ASD research cohorts with enough power to detect very rare causative or contributory CNVs, data from larger clinical cohorts can be used to infer the likely clinical significance of CNVs in ASD.
Revolutionary changes in sequencing technology and the desire to develop therapeutics for rare diseases have led to the generation of an enormous amount of genomic data in the last 5 years. Large-scale sequencing done in both research and diagnostic laboratories has linked many new genes to rare diseases, but has also generated a number of variants that we cannot interpret today. It is clear that we remain a long way from a complete understanding of the genomic variation in the human genome and its association with human health and disease. Recent studies identified susceptibility markers to infectious diseases and also the contribution of rare variants to complex diseases in different populations. The sequencing revolution has also led to the creation of a large number of databases that act as "keepers" of data, and in many cases give an interpretation of the effect of the variant. This interpretation is based on reports in the literature, prediction models, and in some cases is accompanied by functional evidence. As we move toward the practice of genomic medicine, and consider its place in "personalized medicine," it is time to ask ourselves how we can aggregate this wealth of data into a single database for multiple users with different goals.
Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated with recombination. Other genes have been identified by GWAS, but those results have not been replicated. In this study, using new datasets, we characterized additional recombination phenotypes to uncover novel candidates and further dissect the role of already known loci. We used three datasets totaling 1562 two-generation families, including 3108 parents with 4304 children. We estimated five different recombination phenotypes including two novel phenotypes (average recombination counts within recombination hotspots and outside of hotspots) using dense SNP array genotype data. We then performed gender-specific and combined-sex genome-wide association studies (GWAS) meta-analyses. We replicated associations for several previously reported recombination genes, including RNF212 and PRDM9. By looking specifically at recombination events outside of hotspots, we showed for the first time that PRDM9 has different effects in males and females. We identified several new candidate loci, particularly for recombination events outside of hotspots. These include regions near the genes SPINK6, EVC2, ARHGAP25, and DLGAP2. This study expands our understanding of human meiotic recombination by characterizing additional features that vary across individuals, and identifying regulatory variants influencing the numbers and locations of recombination events.
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Prataydipta Rudra;
K. Alaine Broadaway;
Erin B. Ware;
Min A. Jhun;
Lawrence F. Bielak;
Wei Zhao;
Jennifer A. Smith;
Patricia A. Peyser;
Sharon L. R. Kardia;
Michael Epstein;
Debashis Ghosh
Many gene mapping studies of complex traits have identified genes or variants that influence multiple phenotypes. With the advent of next-generation sequencing technology, there has been substantial interest in identifying rare variants in genes that possess cross-phenotype effects. In the presence of such effects, modeling both the phenotypes and rare variants collectively using multivariate models can achieve higher statistical power compared to univariate methods that either model each phenotype separately or perform separate tests for each variant. Several studies collect phenotypic data over time and using such longitudinal data can further increase the power to detect genetic associations. Although rare-variant approaches exist for testing cross-phenotype effects at a single time point, there is no analogous method for performing such analyses using longitudinal outcomes. In order to fill this important gap, we propose an extension of Gene Association with Multiple Traits (GAMuT) test, a method for cross-phenotype analysis of rare variants using a framework based on the distance covariance. The approach allows for both binary and continuous phenotypes and can also adjust for covariates. Our simple adjustment to the GAMuT test allows it to handle longitudinal data and to gain power by exploiting temporal correlation. The approach is computationally efficient and applicable on a genome-wide scale due to the use of a closed-form test whose significance can be evaluated analytically. We use simulated data to demonstrate that our method has favorable power over competing approaches and also apply our approach to exome chip data from the Genetic Epidemiology Network of Arteriopathy.
Accurately selecting relevant alleles in large sequencing experiments remains technically challenging. Bystro (https://bystro.io/ ) is the first online, cloud-based application that makes variant annotation and filtering accessible to all researchers for terabyte-sized whole-genome experiments containing thousands of samples. Its key innovation is a general-purpose, natural-language search engine that enables users to identify and export alleles and samples of interest in milliseconds. The search engine dramatically simplifies complex filtering tasks that previously required programming experience or specialty command-line programs. Critically, Bystro's annotation and filtering capabilities are orders of magnitude faster than previous solutions, saving weeks of processing time for large experiments.
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Derralynn A Hughes;
Kathleen Nicholls;
Suma Shankar;
Gere Sunder-Plassmann;
David Koeller;
Khan Nedd;
Gerard Vockley;
Takashi Hamazaki;
Robin Lachmann;
Toya Ohashi;
Iacopo Olivotto;
Norio Sakai;
Patrick Deegan;
David Dimmock;
François Eyskens;
Dominique P. Germain;
Ozlem Goker-Alpan;
Eric Hachulla;
Ana Jovanovic;
Charles M Lourenco;
Ichiei Narita;
William Wilcox
Background Fabry disease is an X-linked lysosomal storage disorder caused by GLA mutations, resulting in α-galactosidase (α-Gal) deficiency and accumulation of lysosomal substrates. Migalastat, an oral pharmacological chaperone being developed as an alternative to intravenous enzyme replacement therapy (ERT), stabilises specific mutant (amenable) forms of α- Gal to facilitate normal lysosomal trafficking. Methods The main objective of the 18-month, randomised, active-controlled ATTRACT study was to assess the effects of migalastat on renal function in patients with Fabry disease previously treated with ERT. Effects on heart, disease substrate, patient-reported outcomes (PROs) and safety were also assessed. Results Fifty-seven adults (56% female) receiving ERT (88% had multiorgan disease) were randomised (1.5:1), based on a preliminary cell-based assay of responsiveness to migalastat, to receive 18 months open-label migalastat or remain on ERT. Four patients had nonamenable mutant forms of α-Gal based on the validated cell-based assay conducted after treatment initiation and were excluded from primary efficacy analyses only. Migalastat and ERT had similar effects on renal function. Left ventricular mass index decreased significantly with migalastat treatment (-6.6 g/m2 (-11.0 to -2.2)); there was no significant change with ERT. Predefined renal, cardiac or cerebrovascular events occurred in 29% and 44% of patients in the migalastat and ERT groups, respectively. Plasma globotriaosylsphingosine remained low and stable following the switch from ERT to migalastat. PROs were comparable between groups. Migalastat was generally safe and well tolerated. Conclusions Migalastat offers promise as a first-in-class oral monotherapy alternative treatment to intravenous ERT for patients with Fabry disease and amenable mutations.
DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high-throughput technologies have enabled genome-wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post-imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait-associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome-wide association study (EWAS).