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Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

Primary effusion lymphoma (PEL) is a largely incurable malignancy of B cell origin with plasmacytic differentiation. Here, we report the identification of a highly effective inhibitor of PEL. This compound, 6-ethylthioinosine (6-ETI), is a nucleoside analog with toxicity to PEL in vitro and in vivo, but not to other lymphoma cell lines tested. We developed and performed resistome analysis, an unbiased approach based on RNA sequencing of resistant subclones, to discover the molecular mechanisms of sensitivity. We found different adenosine kinase -inactivating (ADK-inactivating) alterations in all resistant clones and determined that ADK is required to phosphorylate and activate 6-ETI. Further, we observed that 6-ETI induces ATP depletion and cell death accompanied by S phase arrest and DNA damage only in ADK-expressing cells. Immunohistochemistry for ADK served as a biomarker approach to identify 6-ETI -sensitive tumors, which we documented for other lymphoid malignancies with plasmacytic features. Notably, multiple myeloma (MM) expresses high levels of ADK, and 6-ETI was toxic to MM cell lines and primary specimens and had a robust antitumor effect in a disseminated MM mouse model. Several nucleoside analogs are effective in treating leukemias and T cell lymphomas, and 6-ETI may fill this niche for the treatment of PEL, plasmablastic lymphoma, MM, and other ADK-expressing cancers., Introduction The [gamma]-herpesvirus KSHV, also called HHV-8, is the etiological agent of Kaposi's sarcoma (KS) (1), multicentric Castleman's disease (2), and primary effusion lymphoma (PEL) (3). KS, the most common [...]

Abstract Background M. tuberculosis is a formidable bacterial pathogen. There is thus an increasing demand on understanding the function and relationship of proteins in various strains of M. tuberculosis. Protein-protein interactions (PPIs) data are crucial for this kind of knowledge. However, the quality of the main available M. tuberculosis PPI datasets is unclear. This hampers the effectiveness of research works that rely on these PPI datasets. Here, we analyze the two main available M. tuberculosis H37Rv PPI datasets. The first dataset is the high-throughput B2H PPI dataset from Wang et al’s recent paper in Journal of Proteome Research. The second dataset is from STRING database, version 8.3, comprising entirely of H37Rv PPIs predicted using various methods. We find that these two datasets have a surprisingly low level of agreement. We postulate the following causes for this low level of agreement: (i) the H37Rv B2H PPI dataset is of low quality; (ii) the H37Rv STRING PPI dataset is of low quality; and/or (iii) the H37Rv STRING PPIs are predictions of other forms of functional associations rather than direct physical interactions. Results To test the quality of these two datasets, we evaluate them based on correlated gene expression profiles, coherent informative GO term annotations, and conservation in other organisms. We observe a significantly greater portion of PPIs in the H37Rv STRING PPI dataset (with score ≥ 770) having correlated gene expression profiles and coherent informative GO term annotations in both interaction partners than that in the H37Rv B2H PPI dataset. Predicted H37Rv interologs derived from non-M. tuberculosis experimental PPIs are much more similar to the H37Rv STRING functional associations dataset (with score ≥ 770) than the H37Rv B2H PPI dataset. H37Rv predicted physical interologs from IntAct also show extremely low similarity with the H37Rv B2H PPI dataset; and this similarity level is much lower than that between the S. aureus MRSA252 predicted physical interologs from IntAct and S. aureus MRSA252 pull-down PPIs. Comparative analysis with several representative two-hybrid PPI datasets in other species further confirms that the H37Rv B2H PPI dataset is of low quality. Next, to test the possibility that the H37Rv STRING PPIs are not purely direct physical interactions, we compare M. tuberculosis H37Rv protein pairs that catalyze adjacent steps in enzymatic reactions to B2H PPIs and predicted PPIs in STRING, which shows it has much lower similarities with the B2H PPIs than with STRING PPIs. This result strongly suggests that the H37Rv STRING PPIs more likely correspond to indirect relationships between protein pairs than to B2H PPIs. For more precise support, we turn to S. cerevisiae for its comprehensively studied interactome. We compare S. cerevisiae predicted PPIs in STRING to three independent protein relationship datasets which respectively comprise PPIs reported in Y2H assays, protein pairs reported to be in the same protein complexes, and protein pairs that catalyze successive reaction steps in enzymatic reactions. Our analysis reveals that S. cerevisiae predicted STRING PPIs have much higher similarity to the latter two types of protein pairs than to two-hybrid PPIs. As H37Rv STRING PPIs are predicted using similar methods as S. cerevisiae predicted STRING PPIs, this suggests that these H37Rv STRING PPIs are more likely to correspond to the latter two types of protein pairs rather than to two-hybrid PPIs as well. Conclusions The H37Rv B2H PPI dataset has low quality. It should not be used as the gold standard to assess the quality of other (possibly predicted) H37Rv PPI datasets. The H37Rv STRING PPI dataset also has low quality; nevertheless, a subset consisting of STRING PPIs with score ≥770 has satisfactory quality. However, these STRING “PPIs” should be interpreted as functional associations, which include a substantial portion of indirect protein interactions, rather than direct physical interactions. These two factors cause the strikingly low similarity between these two main H37Rv PPI datasets. The results and conclusions from this comparative analysis provide valuable guidance in using these M. tuberculosis H37Rv PPI datasets in subsequent studies for a wide range of purposes.

Allele frequency estimates in admixed populations, such as Hispanics and Latinos, rely on the sample's specific admixture composition and thus may differ between two seemingly similar populations. However, ancestry-specific allele frequencies, i.e., pertaining to the ancestral populations of an admixed group, may be particularly useful for prioritizing genetic variants for genetic discovery and personalized genomic health. We developed a method, ancestry-specific allele frequency estimation in admixed populations (AFA), to estimate the frequencies of biallelic variants in admixed populations with an unlimited number of ancestries. AFA uses maximum-likelihood estimation by modeling the conditional probability of having an allele given proportions of genetic ancestries. It can be applied using either local ancestry interval proportions encompassing the variant (local-ancestry-specific allele frequency estimations in admixed populations [LAFAs]) or global proportions of genetic ancestries (global-ancestry-specific allele frequency estimations in admixed populations [GAFAs]), which are easier to compute and are more widely available. Simulations and comparisons to existing software demonstrated the high accuracy of the method. We implemented AFA on high-quality imputed data of ∼9,000 Hispanics and Latinos from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an understudied, admixed population with three predominant continental ancestries: Amerindian, European, and African. Comparison of the European and African estimated frequencies to the respective gnomAD frequencies demonstrated high correlations (Pearson R 2 = 0.97-0.99). We provide a genome-wide dataset of the estimated ancestry-specific allele frequencies for available variants with allele frequency between 5% and 95% in at least one of the three ancestral populations. Association analysis of Amerindian-enriched variants with cardiometabolic traits identified five loci associated with lipid traits in Hispanics and Latinos, demonstrating the utility of ancestry-specific allele frequencies in admixed populations., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)