Return of genetic research results in 21,532 individuals with autism.

PURPOSE: The aim of this study is to identify likely pathogenic (LP) and pathogenic (P) genetic results for autism that can be returned to participants in SPARK (SPARKforAutism.org): a large recontactable cohort of people with autism in the United States. We also describe the process to return these clinically confirmed genetic findings. METHODS: We present results from microarray genotyping and exome sequencing (ES) of 21,532 individuals with autism and 17,785 of their parents. We returned LP and P (American College of Medical genetics (ACMG) criteria) copy number variants (CNVs), chromosomal aneuploidies, and variants in genes with strong evidence of association with autism and intellectual disability. RESULTS: We identified 1903 'returnable' LP/P variants in 1861 individuals with autism (8.6%). 89.5% of these variants were not known to participants. The diagnostic genetic result was returned to 589 participants (53% of those contacted). Features associated with a higher probability of having a returnable result include cognitive and medically complex features, being female, being White (versus non-White) and being diagnosed more than 20 years ago. We also find results among autistics across the spectrum, as well as in transmitting parents with neuropsychiatric features but no autism diagnosis. CONCLUSION: SPARK offers an opportunity to assess returnable results among autistic people who have not been ascertained clinically. SPARK also provides practical experience returning genetic results for a behavioral condition at a large scale.

Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes.

To capture the full spectrum of genetic risk for autism, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 autism cases, including 35,130 new cases recruited online by SPARK. We identified 60 genes with exome-wide significance (P < 2.5 × 10-6), including five new risk genes (NAV3, ITSN1, MARK2, SCAF1 and HNRNPUL2). The association of NAV3 with autism risk is primarily driven by rare inherited loss-of-function (LoF) variants, with an estimated relative risk of 4, consistent with moderate effect. Autistic individuals with LoF variants in the four moderate-risk genes (NAV3, ITSN1, SCAF1 and HNRNPUL2; n = 95) have less cognitive impairment than 129 autistic individuals with LoF variants in highly penetrant genes (CHD8, SCN2A, ADNP, FOXP1 and SHANK3) (59% vs 88%, P = 1.9 × 10-6). Power calculations suggest that much larger numbers of autism cases are needed to identify additional moderate-risk genes.

Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes.

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. In addition, we identified variants that are possibly associated with ASD in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.1 provides statistical support for 26 ASD risk genes. While most of these genes are already known ASD risk genes, BRSK2 has the strongest statistical support and reaches genome-wide significance as a risk gene for ASD (p-value = 2.3e-06). Future studies leveraging the thousands of individuals with ASD who have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate ASD research that incorporates genetic etiology.

Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment.

BACKGROUND: Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in inhabitants from low-income countries and in visitors to these countries. The impact of the human intestinal microbiota on the initiation and progression of ETEC diarrhea is not yet well understood. RESULTS: We used 16S rRNA (ribosomal RNA) gene sequencing to study changes in the fecal microbiota of 12 volunteers during a human challenge study with ETEC (H10407) and subsequent treatment with ciprofloxacin. Five subjects developed severe diarrhea and seven experienced few or no symptoms. Diarrheal symptoms were associated with high concentrations of fecal E. coli as measured by quantitative culture, quantitative PCR, and normalized number of 16S rRNA gene sequences. Large changes in other members of the microbiota varied greatly from individual to individual, whether or not diarrhea occurred. Nonetheless the variation within an individual was small compared to variation between individuals. Ciprofloxacin treatment reorganized microbiota populations; however, the original structure was largely restored at one and three month follow-up visits. CONCLUSION: Symptomatic ETEC infections, but not asymptomatic infections, were associated with high fecal concentrations of E. coli. Both infection and ciprofloxacin treatment caused variable changes in other bacteria that generally reverted to baseline levels after three months.

Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition.

BACKGROUND: Diarrheal diseases continue to contribute significantly to morbidity and mortality in infants and young children in developing countries. There is an urgent need to better understand the contributions of novel, potentially uncultured, diarrheal pathogens to severe diarrheal disease, as well as distortions in normal gut microbiota composition that might facilitate severe disease. RESULTS: We use high throughput 16S rRNA gene sequencing to compare fecal microbiota composition in children under five years of age who have been diagnosed with moderate to severe diarrhea (MSD) with the microbiota from diarrhea-free controls. Our study includes 992 children from four low-income countries in West and East Africa, and Southeast Asia. Known pathogens, as well as bacteria currently not considered as important diarrhea-causing pathogens, are positively associated with MSD, and these include Escherichia/Shigella, and Granulicatella species, and Streptococcus mitis/pneumoniae groups. In both cases and controls, there tend to be distinct negative correlations between facultative anaerobic lineages and obligate anaerobic lineages. Overall genus-level microbiota composition exhibit a shift in controls from low to high levels of Prevotella and in MSD cases from high to low levels of Escherichia/Shigella in younger versus older children; however, there was significant variation among many genera by both site and age. CONCLUSIONS: Our findings expand the current understanding of microbiota-associated diarrhea pathogenicity in young children from developing countries. Our findings are necessarily based on correlative analyses and must be further validated through epidemiological and molecular techniques.

De novo likelihood-based measures for comparing genome assemblies.

BACKGROUND: The current revolution in genomics has been made possible by software tools called genome assemblers, which stitch together DNA fragments "read" by sequencing machines into complete or nearly complete genome sequences. Despite decades of research in this field and the development of dozens of genome assemblers, assessing and comparing the quality of assembled genome sequences still relies on the availability of independently determined standards, such as manually curated genome sequences, or independently produced mapping data. These "gold standards" can be expensive to produce and may only cover a small fraction of the genome, which limits their applicability to newly generated genome sequences. Here we introduce a de novo probabilistic measure of assembly quality which allows for an objective comparison of multiple assemblies generated from the same set of reads. We define the quality of a sequence produced by an assembler as the conditional probability of observing the sequenced reads from the assembled sequence. A key property of our metric is that the true genome sequence maximizes the score, unlike other commonly used metrics. RESULTS: We demonstrate that our de novo score can be computed quickly and accurately in a practical setting even for large datasets, by estimating the score from a relatively small sample of the reads. To demonstrate the benefits of our score, we measure the quality of the assemblies generated in the GAGE and Assemblathon 1 assembly "bake-offs" with our metric. Even without knowledge of the true reference sequence, our de novo metric closely matches the reference-based evaluation metrics used in the studies and outperforms other de novo metrics traditionally used to measure assembly quality (such as N50). Finally, we highlight the application of our score to optimize assembly parameters used in genome assemblers, which enables better assemblies to be produced, even without prior knowledge of the genome being assembled. CONCLUSION: Likelihood-based measures, such as ours proposed here, will become the new standard for de novo assembly evaluation.

MetAMOS: a modular and open source metagenomic assembly and analysis pipeline.

We describe MetAMOS, an open source and modular metagenomic assembly and analysis pipeline. MetAMOS represents an important step towards fully automated metagenomic analysis, starting with next-generation sequencing reads and producing genomic scaffolds, open-reading frames and taxonomic or functional annotations. MetAMOS can aid in reducing assembly errors, commonly encountered when assembling metagenomic samples, and improves taxonomic assignment accuracy while also reducing computational cost. MetAMOS can be downloaded from: https://github.com/treangen/MetAMOS.

Inferring viral quasispecies spectra from 454 pyrosequencing reads.

BACKGROUND: RNA viruses infecting a host usually exist as a set of closely related sequences, referred to as quasispecies. The genomic diversity of viral quasispecies is a subject of great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software was originally designed for single genome assembly and cannot be used to simultaneously assemble and estimate the abundance of multiple closely related quasispecies sequences. RESULTS: In this paper, we introduce a new Viral Spectrum Assembler (ViSpA) method for quasispecies spectrum reconstruction and compare it with the state-of-the-art ShoRAH tool on both simulated and real 454 pyrosequencing shotgun reads from HCV and HIV quasispecies. Experimental results show that ViSpA outperforms ShoRAH on simulated error-free reads, correctly assembling 10 out of 10 quasispecies and 29 sequences out of 40 quasispecies. While ShoRAH has a significant advantage over ViSpA on reads simulated with sequencing errors due to its advanced error correction algorithm, ViSpA is better at assembling the simulated reads after they have been corrected by ShoRAH. ViSpA also outperforms ShoRAH on real 454 reads. Indeed, 7 most frequent sequences reconstructed by ViSpA from a real HCV dataset are viable (do not contain internal stop codons), and the most frequent sequence was within 1% of the actual open reading frame obtained by cloning and Sanger sequencing. In contrast, only one of the sequences reconstructed by ShoRAH is viable. On a real HIV dataset, ShoRAH correctly inferred only 2 quasispecies sequences with at most 4 mismatches whereas ViSpA correctly reconstructed 5 quasispecies with at most 2 mismatches, and 2 out of 5 sequences were inferred without any mismatches. ViSpA source code is available at http://alla.cs.gsu.edu/~software/VISPA/vispa.html. CONCLUSIONS: ViSpA enables accurate viral quasispecies spectrum reconstruction from 454 pyrosequencing reads. We are currently exploring extensions applicable to the analysis of high-throughput sequencing data from bacterial metagenomic samples and ecological samples of eukaryote populations.