Stacey Gabriel

Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of …

Rationale Chronic obstructive pulmonary disease (COPD) patients demonstrate marked heterogeneity with respect to emphysema, mortality, exacerbations, lung function decline, and other disease-related outcomes. Omic Scores (OS) estimate the cumulative contribution for omics such as the transcriptome, proteome, and metabolome to a particular trait. In this study, we aimed to assess the predictive value of OSs for COPD-related traits in both smoking-enriched and general population cohorts. Methods We included Genetic Epidemiology of COPD (COPDGene) and Multi-Ethnic Study of Atherosclerosis (MESA) participants with RNA-sequencing, proteomic, and metabolomic data. We split COPDGene into training and testing datasets (80: 20). Within the training set, we constructed OS using elastic net regression (with 10-fold cross-validation) for the following traits/outcomes measured coincident with omics …

MethodsVariants in gnomAD-SV were detected using two tools within the genome analysis toolkit (GATK); GATK-SV for GS and GATK-gCNV for ES. Briefly, GATK-SV discovers, resolves, and genotypes SVs by harmonizing outputs from five algorithms, then adjudicating and re-genotyping from raw read evidence. GATK-SV is publicly available on GitHub (https://github. com/broadinstitute/gatk-sv) and has been designed as cloud deployable to allow parallel scaling. GATK-gCNV is an SV discovery tool for ES that applies a Bayesian approach to adjust for biasing factors from exome capture while controlling for technical and systematic differences across samples. Note, these ES calls are limited to rare copy number variants (< 1% site frequency) spanning> 2 exons. Benchmarking for each dataset was performed using orthogonal data from chromosomal microarrays and long read sequencing with both methods …

Full-length RNA-sequencing methods using long-read technologies can capture complete transcript isoforms, but their throughput is limited. We introduce multiplexed arrays isoform sequencing (MAS-ISO-seq), a technique for programmably concatenating complementary DNAs (cDNAs) into molecules optimal for long-read sequencing, increasing the throughput >15-fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. When applied to single-cell RNA sequencing of tumor-infiltrating T cells, MAS-ISO-seq demonstrated a 12- to 32-fold increase in the discovery of differentially spliced genes.

Comprehensively mapping the genetic basis of human disease across diverse individuals is a longstanding goal for the field of human genetics.1-4 The All of Us Research Program is a longitudinal cohort aiming to enroll a diverse group of at least one million individuals across the United States to accelerate biomedical research and improve human health.5,6 Here we describe the program’s genomics data release of 245,388 clinical grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities historically underrepresented in biomedical research and 46% are individuals from underrepresented racial and ethnic minorities. All of Us identified >1 billion genetic variants, including >151 million previously unreported genetic variants, >1.8 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record …

In the version of the article originally published, some of the oligonucleotide sequences in Supplementary Table 4, on the “21 viruses” and “RVP” tabs, were mislabeled. The Supplementary Tables file has now been corrected.

In the version of this article initially published, data points did not appear in Supplementary Figs. 6–8, and are now included in the online version of the Supplementary Information.

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders, , –, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)—the largest public open-access human genome allele frequency reference dataset—and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome …