Shawn Levy

Using five complementary short- and long-read sequencing technologies, we phased and assembled >95% of each diploid human genome in a four-generation, 28-member family (CEPH 1463) allowing us to systematically assess de novo mutations (DNMs) and recombination. From this family, we estimate an average of 192 DNMs per generation, including 75.5 de novo single-nucleotide variants (SNVs), 7.4 non-tandem repeat indels, 79.6 de novo indels or structural variants (SVs) originating from tandem repeats, 7.7 centromeric de novo SVs and SNVs, and 12.4 de novo Y chromosome events per generation. STRs and VNTRs are the most mutable with 32 loci exhibiting recurrent mutation through the generations. We accurately assemble 288 centromeres and six Y chromosomes across the generations, documenting de novo SVs, and demonstrate that the DNM rate varies by an order of magnitude depending on repeat content, length, and sequence identity. We show a strong paternal bias (75-81%) for all forms of germline DNM, yet we estimate that 17% of de novo SNVs are postzygotic in origin with no paternal bias. We place all this variation in the context of a high-resolution recombination map (∼3.5 kbp breakpoint resolution). We observe a strong maternal recombination bias (1.36 maternal:paternal ratio) with a consistent reduction in the number of crossovers with increasing paternal (r=0.85) and maternal (r=0.65) age. However, we observe no correlation between meiotic crossover locations and de novo SVs, arguing against non-allelic homologous recombination as a predominant mechanism. The use of multiple orthogonal technologies, near-telomere-to-telomere phased genome assemblies, and a multi-generation family to assess transmission has created the most comprehensive, publicly available “truth set” of all classes of genomic variants. The resource can be used to test and benchmark new algorithms and technologies to understand the most fundamental processes underlying human genetic variation.

The All of Us (AoU) initiative aims to sequence the genomes of over one million Americans from diverse ethnic backgrounds to improve personalized medical care. In a recent technical pilot, we compare the performance of traditional short-read sequencing with long-read sequencing in a small cohort of samples from the HapMap project and two AoU control samples representing eight datasets. Our analysis reveals substantial differences in the ability of these technologies to accurately sequence complex medically relevant genes, particularly in terms of gene coverage and pathogenic variant identification. We also consider the advantages and challenges of using low coverage sequencing to increase sample numbers in large cohort analysis. Our results show that HiFi reads produce the most accurate results for both small and large variants. Further, we present a cloud-based pipeline to optimize SNV, indel and SV calling at scale for long-reads analysis. These results lead to widespread improvements across AoU.

We investigate the new sequencing technology Avidity from Element Biosciences. We show that Element whole genome sequencing achieves higher mapping and variant calling accuracy compared to Illumina sequencing at the same coverage, with larger differences at lower coverages (20x-30x). We quantify base error rates of Element reads, finding lower error rates, especially in homopolymer and tandem repeat regions. We use Element’s ability to generate paired end sequencing with longer insert sizes than typical short–read sequencing. We show that longer insert sizes result in even higher accuracy, with long insert Element sequencing giving noticeably more accurate genome analyses at all coverages.

Islet-enriched transcription factors (TFs) exert broad control over cellular processes in pancreatic α and β cells, and changes in their expression are associated with developmental state and diabetes. However, the implications of heterogeneity in TF expression across islet cell populations are not well understood. To define this TF heterogeneity and its consequences for cellular function, we profiled more than 40,000 cells from normal human islets by single-cell RNA-Seq and stratified α and β cells based on combinatorial TF expression. Subpopulations of islet cells coexpressing ARX/MAFB (α cells) and MAFA / MAFB (β cells) exhibited greater expression of key genes related to glucose sensing and hormone secretion relative to subpopulations expressing only one or neither TF. Moreover, all subpopulations were identified in native pancreatic tissue from multiple donors. By Patch-Seq, MAFA / MAFB -coexpressing β cells showed enhanced electrophysiological activity. Thus, these results indicate that combinatorial TF expression in islet α and β cells predicts highly functional, mature subpopulations.

Assessing the reproducibility, accuracy and utility of massively parallel DNA sequencing platforms remains an ongoing challenge. Here the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Study benchmarks the performance of a set of sequencing instruments (HiSeq/NovaSeq/paired-end 2 × 250-bp chemistry, Ion S5/Proton, PacBio circular consensus sequencing (CCS), Oxford Nanopore Technologies PromethION/MinION, BGISEQ-500/MGISEQ-2000 and GS111) on human and bacterial reference DNA samples. Among short-read instruments, HiSeq 4000 and X10 provided the most consistent, highest genome coverage, while BGI/MGISEQ provided the lowest sequencing error rates. The long-read instrument PacBio CCS had the highest reference-based mapping rate and lowest non-mapping rate. The two long-read platforms PacBio CCS and PromethION/MinION showed the best sequence mapping in repeat-rich areas and across homopolymers. NovaSeq 6000 using 2 × 250-bp read chemistry was the most robust instrument for capturing known insertion/deletion events. This study serves as a benchmark for current genomics technologies, as well as a resource to inform experimental design and next-generation sequencing variant calling.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin–angiotensin–aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinctACE2expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Islet-enriched transcription factors (TFs) exert broad control over cellular processes in pancreatic α and β cells and changes in their expression are associated with developmental state and diabetes. However, the implications of heterogeneity in TF expression across islet cell populations are not well understood. To define this TF heterogeneity and its consequences for cellular function, we profiled >40,000 cells from normal human islets by scRNA-seq and stratified α and β cells based on combinatorial TF expression. Subpopulations of islet cells co-expressing ARX/MAFB (α cells) and MAFA / MAFB (β cells) exhibited greater expression of key genes related to glucose sensing and hormone secretion relative to subpopulations expressing only one or neither TF. Moreover, all subpopulations were identified in native pancreatic tissue from multiple donors. By Patch-seq, MAFA / MAFB co-expressing β cells showed enhanced electrophysiological activity. Thus, these results indicate combinatorial TF expression in islet α and β cells predicts highly functional, mature subpopulations.

Bipolar disorder (BD) is a serious mental illness with substantial common variant heritability. However, the role of rare coding variation in BD is not well established. We examined the protein-coding (exonic) sequences of 3,987 unrelated individuals with BD and 5,322 controls of predominantly European ancestry across four cohorts from the Bipolar Sequencing Consortium (BSC). We assessed the burden of rare, protein-altering, single nucleotide variants classified as pathogenic or likely pathogenic (P-LP) both exome-wide and within several groups of genes with phenotypic or biologic plausibility in BD. While we observed an increased burden of rare coding P-LP variants within 165 genes identified as BD GWAS regions in 3,987 BD cases (meta-analysis OR = 1.9, 95% CI = 1.3–2.8, one-sided p = 6.0 × 10⁻⁴), this enrichment did not replicate in an additional 9,929 BD cases and 14,018 controls (OR = 0.9, one-side p = 0.70). Although BD shares common variant heritability with schizophrenia, in the BSC sample we did not observe a significant enrichment of P-LP variants in SCZ GWAS genes, in two classes of neuronal synaptic genes (RBFOX2 and FMRP) associated with SCZ or in loss-of-function intolerant genes. In this study, the largest analysis of exonic variation in BD, individuals with BD do not carry a replicable enrichment of rare P-LP variants across the exome or in any of several groups of genes with biologic plausibility. Moreover, despite a strong shared susceptibility between BD and SCZ through common genetic variation, we do not observe an association between BD risk and rare P-LP coding variants in genes known to modulate risk for SCZ.