The design of mapping populations: Impacts of geographic scale on genetic architecture and mapping efficacy for defense and immunity.

Genome-wide association studies (GWAS) have yielded tremendous insight into the genetic architecture of trait variation. However, the collections of loci they uncover are far from exhaustive. As many of the complicating factors that confound or limit the efficacy of GWAS are exaggerated over broad geographic scales, a shift toward more analyses using mapping panels sampled from narrow geographic localities ("local" populations) could provide novel, complementary insights. Here, we present an overview of the major complicating factors, review mounting evidence from genomic analyses that these factors are pervasive, and synthesize theoretical and empirical evidence for the power of GWAS in local populations.

Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution.

Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19-such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events-suggest new directions for improving upon existing population genetic models.

Locus-Specific Characterization of Human Endogenous Retrovirus Expression in Prostate, Breast, and Colon Cancers.

Human endogenous retroviruses (HERV) have been implicated in a variety of diseases including cancers. Recent research implicates HERVs in epigenetic gene regulation. Here we utilize a recently developed bioinformatics tool for identifying HERV expression at the locus-specific level to identify differential expression of HERVs in matched tumor-normal RNA-sequencing (RNA-seq) data from The Cancer Genome Atlas. Data from 52 prostate cancer, 111 breast cancer, and 24 colon cancer cases were analyzed. Locus-specific analysis identified active HERV elements and differentially expressed HERVs in prostate cancer, breast cancer, and colon cancer. In addition, differentially expressed host genes were identified across prostate, breast, and colon cancer datasets, respectively, including several involved in demethylation and antiviral response pathways, supporting previous findings regarding the pathogenic mechanisms of HERVs. A majority of differentially expressed HERVs intersected protein coding genes or lncRNAs in each dataset, and a subset of differentially expressed HERVs intersected differentially expressed genes in prostate, breast, and colon cancers, providing evidence towards regulatory function. Finally, patterns in HERV expression were identified in multiple cancer types, with 155 HERVs differentially expressed in all three cancer types. This analysis extends previous results identifying HERV transcription in cancer RNA-seq datasets to a locus-specific level, and in doing so provides a foundation for future studies investigating the functional role of HERV in cancers and identifies a number of novel targets for cancer biomarkers and immunotherapy. SIGNIFICANCE: Expressed human endogenous retroviruses are mapped at locus-specific resolution and linked to specific pathways to identify potential biomarkers and therapeutic targets in prostate, breast, and colon cancers.

HAPHPIPE: Haplotype Reconstruction and Phylodynamics for Deep Sequencing of Intrahost Viral Populations.

Deep sequencing of viral populations using next-generation sequencing (NGS) offers opportunities to understand and investigate evolution, transmission dynamics, and population genetics. Currently, the standard practice for processing NGS data to study viral populations is to summarize all the observed sequences from a sample as a single consensus sequence, thus discarding valuable information about the intrahost viral molecular epidemiology. Furthermore, existing analytical pipelines may only analyze genomic regions involved in drug resistance, thus are not suited for full viral genome analysis. Here, we present HAPHPIPE, a HAplotype and PHylodynamics PIPEline for genome-wide assembly of viral consensus sequences and haplotypes. The HAPHPIPE protocol includes modules for quality trimming, error correction, de novo assembly, alignment, and haplotype reconstruction. The resulting consensus sequences, haplotypes, and alignments can be further analyzed using a variety of phylogenetic and population genetic software. HAPHPIPE is designed to provide users with a single pipeline to rapidly analyze sequences from viral populations generated from NGS platforms and provide quality output properly formatted for downstream evolutionary analyses.

Validation of Variant Assembly Using HAPHPIPE with Next-Generation Sequence Data from Viruses.

Next-generation sequencing (NGS) offers a powerful opportunity to identify low-abundance, intra-host viral sequence variants, yet the focus of many bioinformatic tools on consensus sequence construction has precluded a thorough analysis of intra-host diversity. To take full advantage of the resolution of NGS data, we developed HAplotype PHylodynamics PIPEline (HAPHPIPE), an open-source tool for the de novo and reference-based assembly of viral NGS data, with both consensus sequence assembly and a focus on the quantification of intra-host variation through haplotype reconstruction. We validate and compare the consensus sequence assembly methods of HAPHPIPE to those of two alternative software packages, HyDRA and Geneious, using simulated HIV and empirical HIV, HCV, and SARS-CoV-2 datasets. Our validation methods included read mapping, genetic distance, and genetic diversity metrics. In simulated NGS data, HAPHPIPE generated pol consensus sequences significantly closer to the true consensus sequence than those produced by HyDRA and Geneious and performed comparably to Geneious for HIV gp120 sequences. Furthermore, using empirical data from multiple viruses, we demonstrate that HAPHPIPE can analyze larger sequence datasets due to its greater computational speed. Therefore, we contend that HAPHPIPE provides a more user-friendly platform for users with and without bioinformatics experience to implement current best practices for viral NGS assembly than other currently available options.

Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between "black box" deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data.

Corrigendum: A 28-Year History of HIV-1 Drug Resistance and Transmission in Washington, DC.

[This corrects the article DOI: 10.3389/fmicb.2019.00369.].

A 28-Year History of HIV-1 Drug Resistance and Transmission in Washington, DC.

Washington, DC consistently has one of the highest annual rates of new HIV-1 diagnoses in the United States over the last 10 years. To guide intervention and prevention strategies to combat DC HIV infection, it is helpful to understand HIV transmission dynamics in a historical context. Toward this aim, we conducted a retrospective study (years 1987-2015) of 3,349 HIV pol sequences (1,026 bp) from 1,996 individuals living in the DC area belonging to three different cohorts. We coupled HIV sequence data with clinical information (sex, risk factor, race/ethnicity, viral load, subtype, anti-retroviral regimen) to identify circulating drug resistant mutations (DRM) and transmission clusters and assess their persistence over time. Of the transmission clusters identified in the DC area, 78.0 and 31.7% involved MSM and heterosexuals, respectively. The longest spread of time for a single cluster was 5 years (2007-2012) using a distance-based network inference approach and 27 years (1987-2014) using a maximum likelihood phylogenetic approach. We found eight subtypes and nine recombinants. Genetic diversity increased steadily over time with a slight peak in 2009 and remained constant thereafter until 2015. Nucleotide diversity also increased over time while relative genetic diversity (BEAST) remained relatively steady over the last 28 years with slight increases since 2000 in subtypes B and C. Sequences from individuals on drug therapy contained the highest total number of DRMs (1,104-1,600) and unique DRMs (63-97) and the highest proportion (>20%) of resistant individuals. Heterosexuals (43.94%), MSM (40.13%), and unknown (44.26%) risk factors showed similar prevalence of DRMs, while injection drug users had a lower prevalence (33.33%). Finally, there was a 60% spike in the number of codons with DRMs between 2007 and 2010. Past patterns of HIV transmission and DRM accumulation over time described here will help to predict future efficacy of ART drugs based on DRMs persisting over time and identify risk groups of interest for prevention and intervention efforts within the DC population. Our results show how longitudinal data can help to understand the temporal dynamics of HIV-1 at the local level.