Removing unwanted variation between samples in Hi-C experiments.

Hi-C data are commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation change across the contact map. We present band-wise normalization and batch correction, a method for normalization and batch correction of Hi-C data and show that it substantially improves comparisons across samples, including in a quantitative trait loci analysis as well as differential enrichment across cell types.

The UGT2A1/UGT2A2 locus is associated with COVID-19-related loss of smell or taste.

Using online surveys, we collected data regarding COVID-19-related loss of smell or taste from 69,841 individuals. We performed a multi-ancestry genome-wide association study and identified a genome-wide significant locus in the vicinity of the UGT2A1 and UGT2A2 genes. Both genes are expressed in the olfactory epithelium and play a role in metabolizing odorants. These findings provide a genetic link to the biological mechanisms underlying COVID-19-related loss of smell or taste.

Assessing stationary distributions derived from chromatin contact maps.

BACKGROUND: The spatial configuration of chromosomes is essential to various cellular processes, notably gene regulation, while architecture related alterations, such as translocations and gene fusions, are often cancer drivers. Thus, eliciting chromatin conformation is important, yet challenging due to compaction, dynamics and scale. However, a variety of recent assays, in particular Hi-C, have generated new details of chromatin structure, spawning a number of novel biological findings. Many findings have resulted from analyses on the level of native contact data as generated by the assays. Alternatively, reconstruction based approaches often proceed by first converting contact frequencies into distances, then generating a three dimensional (3D) chromatin configuration that best recapitulates these distances. Subsequent analyses can enrich contact level analyses via superposition of genomic attributes on the reconstruction. But, such advantages depend on the accuracy of the reconstruction which, absent gold standards, is inherently difficult to assess. Attempts at accuracy evaluation have relied on simulation and/or FISH imaging that typically features a handful of low resolution probes. While newly advanced multiplexed FISH imaging offers possibilities for refined 3D reconstruction accuracy evaluation, availability of such data is limited due to assay complexity and the resolution thereof is appreciably lower than the reconstructions being assessed. Accordingly, there is demand for new methods of reconstruction accuracy appraisal. RESULTS: Here we explore the potential of recently proposed stationary distributions, hereafter StatDns, derived from Hi-C contact matrices, to serve as a basis for reconstruction accuracy assessment. Current usage of such StatDns has focussed on the identification of highly interactive regions (HIRs): computationally defined regions of the genome purportedly involved in numerous long-range intra-chromosomal contacts. Consistent identification of HIRs would be informative with respect to inferred 3D architecture since the corresponding regions of the reconstruction would have an elevated number of k nearest neighbors (kNNs). More generally, we anticipate a monotone decreasing relationship between StatDn values and kNN distances. After initially evaluating the reproducibility of StatDns across replicate Hi-C data sets, we use this implied StatDn - kNN relationship to gauge the utility of StatDns for reconstruction validation, making recourse to both real and simulated examples. CONCLUSIONS: Our analyses demonstrate that, as constructed, StatDns do not provide a suitable measure for assessing the accuracy of 3D genome reconstructions. Whether this is attributable to specific choices surrounding normalization in defining StatDns or to the logic underlying their very formulation remains to be determined.

Common DNA sequence variation influences 3-dimensional conformation of the human genome.

BACKGROUND: The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. RESULTS: To address these questions, we perform Hi-C on lymphoblastoid cell lines from 20 individuals. We identify thousands of regions across the genome where 3D chromatin conformation varies between individuals and find that this variation is often accompanied by variation in gene expression, histone modifications, and transcription factor binding. Moreover, we find that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality, and density of local cis contacts. We map hundreds of quantitative trait loci associated with 3D chromatin features and find evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. CONCLUSION: Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized.

Reconstitution of Peripheral T Cells by Tissue-Derived CCR4+ Central Memory Cells Following HIV-1 Antiretroviral Therapy.

BACKGROUND: Highly active antiretroviral therapy induces clinical benefits to HIV-1 infected individuals, which can be striking in those with progressive disease. Improved survival and decreased incidence of opportunistic infections go hand in hand with a suppression of the plasma viral load, an increase in peripheral CD4+ T-cell counts, as well as a reduction in the activation status of both CD4+ and CD8+ T cells. METHODS: We investigated T-cell dynamics during ART by polychromatic flow cytometry in total as well as in HIV-1-specific CD4+ and CD8+ T cells in patients with advanced disease. We also measured gene expression by single cell transcriptomics to assess functional state. RESULTS: The cytokine pattern of HIV-specific CD8+ T cells was not altered after ART, though their magnitude decreased significantly as the plasma viral load was suppressed to undetectable levels. Importantly, while CD4+ T cell numbers increased substantially during the first year, the population did not normalize: the increases were largely due to expansion of mucosal-derived CCR4+ CD4+ TCM; transcriptomic analysis revealed that these are not classical Th2-type cells. CONCLUSION: The apparent long-term normalization of CD4+ T-cell numbers following ART does not comprise a normal balance of functionally distinct cells, but results in a dramatic Th2 shift of the reconstituting immune system.

flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data.

Modern flow cytometry systems can be coupled to plate readers for high-throughput acquisition. These systems allow hundreds of samples to be analyzed in a single day. Quality control of the data remains challenging, however, and is further complicated when a large number of parameters is measured in an experiment. Our examination of 29,228 publicly available FCS files from laboratories worldwide indicates 13.7% have a fluorescence anomaly. In particular, fluorescence measurements for a sample over the collection time may not remain stable due to fluctuations in fluid dynamics; the impact of instabilities may differ between samples and among parameters. Therefore, we hypothesized that tracking cell populations (which represent a summary of all parameters) in centered log ratio space would provide a sensitive and consistent method of quality control. Here, we present flowClean, an algorithm to track subset frequency changes within a sample during acquisition, and flag time periods with fluorescence perturbations leading to the emergence of false populations. Aberrant time periods are reported as a new parameter and added to a revised data file, allowing users to easily review and exclude those events from further analysis. We apply this method to proof-of-concept datasets and also to a subset of data from a recent vaccine trial. The algorithm flags events that are suspicious by visual inspection, as well as those showing more subtle effects that might not be consistently flagged by investigators reviewing the data manually, and out-performs the current state-of-the-art. flowClean is available as an R package on Bioconductor, as a module on the free-to-use GenePattern web server, and as a plugin for FlowJo X. © 2016 International Society for Advancement of Cytometry.