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Fewer than half of individuals with a suspected Mendelian or monogenic condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control data sets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project (1KGP) Oxford Nanopore Technologies Sequencing Consortium aims to generate LRS data from at least 800 of the 1KGP samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37× and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs. 

Abstract Studies of model organisms have provided important insights into how natural genetic differences shape trait variation. These discoveries are driven by the growing availability of genomes and the expansive experimental toolkits afforded to researchers using these species. For example, Caenorhabditis elegans is increasingly being used to identify and measure the effects of natural genetic variants on traits using quantitative genetics. Since 2016, the C. elegans Natural Diversity Resource (CeNDR) has facilitated many of these studies by providing an archive of wild strains, genome-wide sequence and variant data for each strain, and a genome-wide association (GWA) mapping portal for the C. elegans community. Here, we present an updated platform, the Caenorhabditis Natural Diversity Resource (CaeNDR), that enables quantitative genetics and genomics studies across the three Caenorhabditis species: C. elegans, C. briggsae and C. tropicalis. The CaeNDR platform hosts several databases that are continually updated by the addition of new strains, whole-genome sequence data and annotated variants. Additionally, CaeNDR provides new interactive tools to explore natural variation and enable GWA mappings. All CaeNDR data and tools are accessible through a freely available web portal located at caendr.org. 

Abstract The publication of the Caenorhabditis briggsae reference genome in 2003 enabled the first comparative genomics studies between C. elegans and C. briggsae, shedding light on the evolution of genome content and structure in the Caenorhabditis genus. However, despite being widely used, the currently available C. briggsae reference genome is substantially less complete and structurally accurate than the C. elegans reference genome. Here, we used high-coverage Oxford Nanopore long-read and chromosome-conformation capture data to generate chromosome-level reference genomes for two C. briggsae strains: QX1410, a new reference strain closely related to the laboratory AF16 strain, and VX34, a highly divergent strain isolated in China. We also sequenced 99 recombinant inbred lines generated from reciprocal crosses between QX1410 and VX34 to create a recombination map and identify chromosomal domains. Additionally, we used both short- and long-read RNA sequencing data to generate high-quality gene annotations. By comparing these new reference genomes to the current reference, we reveal that hyper-divergent haplotypes cover large portions of the C. briggsae genome, similar to recent reports in C. elegans and C. tropicalis. We also show that the genomes of selfing Caenorhabditis species have undergone more rearrangement than their outcrossing relatives, which has biased previous estimates of rearrangement rate in Caenorhabditis. These new genomes provide a substantially improved platform for comparative genomics in Caenorhabditis and narrow the gap between the quality of genomic resources available for C. elegans and C. briggsae.