Functional Segregation of Overlapping Genes in HIV.

Overlapping genes pose an evolutionary dilemma as one DNA sequence evolves under the selection pressures of multiple proteins. Here, we perform systematic statistical and mutational analyses of the overlapping HIV-1 genes tat and rev and engineer exhaustive libraries of non-overlapped viruses to perform deep mutational scanning of each gene independently. We find a "segregated" organization in which overlapped sites encode functional residues of one gene or the other, but never both. Furthermore, this organization eliminates unfit genotypes, providing a fitness advantage to the population. Our comprehensive analysis reveals the extraordinary manner in which HIV minimizes the constraint of overlapping genes and repurposes that constraint to its own advantage. Thus, overlaps are not just consequences of evolutionary constraints, but rather can provide population fitness advantages.

Genetic Ancestry and Natural Selection Drive Population Differences in Immune Responses to Pathogens.

Individuals from different populations vary considerably in their susceptibility to immune-related diseases. To understand how genetic variation and natural selection contribute to these differences, we tested for the effects of African versus European ancestry on the transcriptional response of primary macrophages to live bacterial pathogens. A total of 9.3% of macrophage-expressed genes show ancestry-associated differences in the gene regulatory response to infection, and African ancestry specifically predicts a stronger inflammatory response and reduced intracellular bacterial growth. A large proportion of these differences are under genetic control: for 804 genes, more than 75% of ancestry effects on the immune response can be explained by a single cis- or trans-acting expression quantitative trait locus (eQTL). Finally, we show that genetic effects on the immune response are strongly enriched for recent, population-specific signatures of adaptation. Together, our results demonstrate how historical selective events continue to shape human phenotypic diversity today, including for traits that are key to controlling infection.

An Mtb-Human Protein-Protein Interaction Map Identifies a Switch between Host Antiviral and Antibacterial Responses.

Although macrophages are armed with potent antibacterial functions, Mycobacterium tuberculosis (Mtb) replicates inside these innate immune cells. Determinants of macrophage intrinsic bacterial control, and the Mtb strategies to overcome them, are poorly understood. To further study these processes, we used an affinity tag purification mass spectrometry (AP-MS) approach to identify 187 Mtb-human protein-protein interactions (PPIs) involving 34 secreted Mtb proteins. This interaction map revealed two factors involved in Mtb pathogenesis-the secreted Mtb protein, LpqN, and its binding partner, the human ubiquitin ligase CBL. We discovered that an lpqN Mtb mutant is attenuated in macrophages, but growth is restored when CBL is removed. Conversely, Cbl-/- macrophages are resistant to viral infection, indicating that CBL regulates cell-intrinsic polarization between antibacterial and antiviral immunity. Collectively, these findings illustrate the utility of this Mtb-human PPI map for developing a deeper understanding of the intricate interactions between Mtb and its host.

The hydrocortisone-responsive urinary metabolome of premature infants.

BACKGROUND: Extremely premature infants are at risk for circulatory collapse or respiratory failure that are often treated with hydrocortisone (HC); however, there is no information on the metabolic consequences of this therapy. METHODS: Longitudinal urine samples from infants <28 weeks gestation in the Trial of Late Surfactant were analyzed by untargeted UHPLC:MS/MS. Fourteen infants who received a tapering course of HC beginning at 3 mg/kg/day for ≥9 days were compared to 14 matched control infants. A secondary cross-sectional analysis by logistic regression used urines from 314 infants. RESULTS: Of 1145 urinary metabolites detected, abundance of 219, representing all the major biochemical pathways, changed at p < 0.05 in the HC-treated group with 90% decreasing; 3 cortisol derivatives increased ~2-fold with HC therapy. Only 11% of regulated metabolites remained responsive at the lowest HC dose. Regulated metabolites included two steroids and thiamin that are associated with lung inflammation in infants. HC responsiveness was confirmed in 57% of metabolites by cross-sectional analysis. CONCLUSIONS: HC treatment of premature infants influenced in a dose-dependent manner abundance of 19% of identified urinary metabolites of diverse biochemical systems, primarily reducing concentrations. These findings indicate that exposure to HC reversibly impacts the nutritional status of premature infants. IMPACT: Hydrocortisone treatment of premature infants with respiratory failure or circulatory collapse alters levels of a subset of urinary metabolites representing all major biochemical pathways. This is the first description of the scope, magnitude, timing and reversibility of metabolomic changes in infants in response to hydrocortisone, and it confirms corticosteroid regulation of three biochemicals that are associated with lung inflammatory status. The findings indicate a dose-dependency of hydrocortisone for metabolomic and anti-inflammatory effects, that prolonged therapy may lower the supply of many nutrients, and that monitoring concentrations of cortisol and inflammation markers may be a useful clinical approach during corticosteroid therapy.

Global mapping of herpesvirus-host protein complexes reveals a transcription strategy for late genes.

Mapping host-pathogen interactions has proven instrumental for understanding how viruses manipulate host machinery and how numerous cellular processes are regulated. DNA viruses such as herpesviruses have relatively large coding capacity and thus can target an extensive network of cellular proteins. To identify the host proteins hijacked by this pathogen, we systematically affinity tagged and purified all 89 proteins of Kaposi's sarcoma-associated herpesvirus (KSHV) from human cells. Mass spectrometry of this material identified over 500 virus-host interactions. KSHV causes AIDS-associated cancers, and its interaction network is enriched for proteins linked to cancer and overlaps with proteins that are also targeted by HIV-1. We found that the conserved KSHV protein ORF24 binds to RNA polymerase II and brings it to viral late promoters by mimicking and replacing cellular TATA-box-binding protein (TBP). This is required for herpesviral late gene expression, a complex and poorly understood phase of the viral lifecycle.

De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population.

De novo mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole-genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called 93,325 single-nucleotide DNMs across 1,465 trios from an array of diverse human populations, and used them to directly estimate and analyze DNM counts, rates, and spectra. We find a significant positive correlation between local recombination rate and local DNM rate, and that DNM rate explains a substantial portion (8.98 to 34.92%, depending on the model) of the genome-wide variation in population-level genetic variation from 41K unrelated TOPMed samples. Genome-wide heterozygosity does correlate with DNM rate, but only explains <1% of variation. While we are underpowered to see small differences, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, we did find significantly fewer DNMs in Amish individuals, even when compared with other Europeans, and even after accounting for parental age and sequencing center. Specifically, we found significant reductions in the number of C→A and T→C mutations in the Amish, which seem to underpin their overall reduction in DNMs. Finally, we calculated near-zero estimates of narrow sense heritability (h2), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.

Ancestry-Dependent Enrichment of Deleterious Homozygotes in Runs of Homozygosity.

Runs of homozygosity (ROH) are important genomic features that manifest when an individual inherits two haplotypes that are identical by descent. Their length distributions are informative about population history, and their genomic locations are useful for mapping recessive loci contributing to both Mendelian and complex disease risk. We have previously shown that ROH, and especially long ROH that are likely the result of recent parental relatedness, are enriched for homozygous deleterious coding variation in a worldwide sample of outbred individuals. However, the distribution of ROH in admixed populations and their relationship to deleterious homozygous genotypes is understudied. Here we analyze whole-genome sequencing data from 1,441 unrelated individuals from self-identified African American, Puerto Rican, and Mexican American populations. These populations are three-way admixed between European, African, and Native American ancestries and provide an opportunity to study the distribution of deleterious alleles partitioned by local ancestry and ROH. We re-capitulate previous findings that long ROH are enriched for deleterious variation genome-wide. We then partition by local ancestry and show that deleterious homozygotes arise at a higher rate when ROH overlap African ancestry segments than when they overlap European or Native American ancestry segments of the genome. These results suggest that, while ROH on any haplotype background are associated with an inflation of deleterious homozygous variation, African haplotype backgrounds may play a particularly important role in the genetic architecture of complex diseases for admixed individuals, highlighting the need for further study of these populations.

Correction: Human demographic history has amplified the effects of background selection across the genome.

[This corrects the article DOI: 10.1371/journal.pgen.1007387.].

Population genetic simulation study of power in association testing across genetic architectures and study designs.

While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.

Human demographic history has amplified the effects of background selection across the genome.

Natural populations often grow, shrink, and migrate over time. Such demographic processes can affect genome-wide levels of genetic diversity. Additionally, genetic variation in functional regions of the genome can be altered by natural selection, which drives adaptive mutations to higher frequencies or purges deleterious ones. Such selective processes affect not only the sites directly under selection but also nearby neutral variation through genetic linkage via processes referred to as genetic hitchhiking in the context of positive selection and background selection (BGS) in the context of purifying selection. While there is extensive literature examining the consequences of selection at linked sites at demographic equilibrium, less is known about how non-equilibrium demographic processes influence the effects of hitchhiking and BGS. Utilizing a global sample of human whole-genome sequences from the Thousand Genomes Project and extensive simulations, we investigate how non-equilibrium demographic processes magnify and dampen the consequences of selection at linked sites across the human genome. When binning the genome by inferred strength of BGS, we observe that, compared to Africans, non-African populations have experienced larger proportional decreases in neutral genetic diversity in strong BGS regions. We replicate these findings in admixed populations by showing that non-African ancestral components of the genome have also been affected more severely in these regions. We attribute these differences to the strong, sustained/recurrent population bottlenecks that non-Africans experienced as they migrated out of Africa and throughout the globe. Furthermore, we observe a strong correlation between FST and the inferred strength of BGS, suggesting a stronger rate of genetic drift. Forward simulations of human demographic history with a model of BGS support these observations. Our results show that non-equilibrium demography significantly alters the consequences of selection at linked sites and support the need for more work investigating the dynamic process of multiple evolutionary forces operating in concert.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants.

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.

A genome-wide association and admixture mapping study of bronchodilator drug response in African Americans with asthma.

Short-acting ß2-adrenergic receptor agonists (SABAs) are the most commonly prescribed asthma medications worldwide. Response to SABAs is measured as bronchodilator drug response (BDR), which varies among racial/ethnic groups in the United States. However, the genetic variation that contributes to BDR is largely undefined in African Americans with asthma. To identify genetic variants that may contribute to differences in BDR in African Americans with asthma, we performed a genome-wide association study (GWAS) of BDR in 949 African-American children with asthma, genotyped with the Axiom World Array 4 (Affymetrix, Santa Clara, CA) followed by imputation using 1000 Genomes phase III genotypes. We used linear regression models adjusting for age, sex, body mass index (BMI) and genetic ancestry to test for an association between BDR and genotype at single-nucleotide polymorphisms (SNPs). To increase power and distinguish between shared vs. population-specific associations with BDR in children with asthma, we performed a meta-analysis across 949 African Americans and 1830 Latinos (total = 2779). Finally, we performed genome-wide admixture mapping to identify regions whereby local African or European ancestry is associated with BDR in African Americans. We identified a population-specific association with an intergenic SNP on chromosome 9q21 that was significantly associated with BDR (rs73650726, p = 7.69 × 10-9). A trans-ethnic meta-analysis across African Americans and Latinos identified three additional SNPs within the intron of PRKG1 that were significantly associated with BDR (rs7903366, rs7070958 and rs7081864, p ≤ 5 × 10-8). Our results failed to replicate in three additional populations of 416 Latinos and 1615 African Americans. Our findings indicate that both population-specific and shared genetic variation contributes to differences in BDR in minority children with asthma, and that the genetic underpinnings of BDR may differ between racial/ethnic groups.

High-resolution network biology: connecting sequence with function.

Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein-protein, genetic and drug-gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.

Global environmental drivers of influenza.

In temperate countries, influenza outbreaks are well correlated to seasonal changes in temperature and absolute humidity. However, tropical countries have much weaker annual climate cycles, and outbreaks show less seasonality and are more difficult to explain with environmental correlations. Here, we use convergent cross mapping, a robust test for causality that does not require correlation, to test alternative hypotheses about the global environmental drivers of influenza outbreaks from country-level epidemic time series. By moving beyond correlation, we show that despite the apparent differences in outbreak patterns between temperate and tropical countries, absolute humidity and, to a lesser extent, temperature drive influenza outbreaks globally. We also find a hypothesized U-shaped relationship between absolute humidity and influenza that is predicted by theory and experiment, but hitherto has not been documented at the population level. The balance between positive and negative effects of absolute humidity appears to be mediated by temperature, and the analysis reveals a key threshold around 75 °F. The results indicate a unified explanation for environmental drivers of influenza that applies globally.

Using genotype array data to compare multi- and single-sample variant calls and improve variant call sets from deep coverage whole-genome sequencing data.

Motivation: Variant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X). Results: We have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illuminas single-sample caller CASAVA, Real Time Genomics multisample variant caller, and the GATK UnifiedGenotyper, respectively. Since NGS sequencing data may be accompanied by genotype data for the same samples, either collected concurrent to sequencing or from a previous study, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g. a different set of criteria to determine quality for rare versus common variants) and thereby provides insight into sequencing characteristics that indicate call quality for variants of different frequencies. Availability and Implementation: Code is available on Github at: https://github.com/suyashss/variant_validation. Contacts: suyashs@stanford.edu or mtaub@jhsph.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Global landscape of HIV-human protein complexes.

Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host's cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV-human protein-protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.

Population genetic simulations of complex phenotypes with implications for rare variant association tests.

Demographic events and natural selection alter patterns of genetic variation within populations and may play a substantial role in shaping the genetic architecture of complex phenotypes and disease. However, the joint impact of these basic evolutionary forces is often ignored in the assessment of statistical tests of association. Here, we provide a simulation-based framework for generating DNA sequences that incorporates selection and demography with flexible models for simulating phenotypic variation (sfs_coder). This tool also allows the user to perform locus-specific simulations by automatically querying annotated genomic functional elements and genetic maps. We demonstrate the effects of evolutionary forces on patterns of genetic variation by simulating recently inferred models of human selection and demography. We use these simulations to show that the demographic model and locus-specific features, such as the proportion of sites under selection, may have practical implications for estimating the statistical power of sequencing-based rare variant association tests. In particular, for some phenotype models, there may be higher power to detect rare variant associations in African populations compared to non-Africans, but power is considerably reduced in regions of the genome with rampant negative selection. Furthermore, we show that existing methods for simulating large samples based on resampling from a small set of observed haplotypes fail to recapitulate the distribution of rare variants in the presence of rapid population growth (as has been observed in several human populations).

Genetic simulation tools for post-genome wide association studies of complex diseases.

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

Genome-wide association study and admixture mapping reveal new loci associated with total IgE levels in Latinos.

BACKGROUND: IgE is a key mediator of allergic inflammation, and its levels are frequently increased in patients with allergic disorders. OBJECTIVE: We sought to identify genetic variants associated with IgE levels in Latinos. METHODS: We performed a genome-wide association study and admixture mapping of total IgE levels in 3334 Latinos from the Genes-environments & Admixture in Latino Americans (GALA II) study. Replication was evaluated in 454 Latinos, 1564 European Americans, and 3187 African Americans from independent studies. RESULTS: We confirmed associations of 6 genes identified by means of previous genome-wide association studies and identified a novel genome-wide significant association of a polymorphism in the zinc finger protein 365 gene (ZNF365) with total IgE levels (rs200076616, P = 2.3 × 10(-8)). We next identified 4 admixture mapping peaks (6p21.32-p22.1, 13p22-31, 14q23.2, and 22q13.1) at which local African, European, and/or Native American ancestry was significantly associated with IgE levels. The most significant peak was 6p21.32-p22.1, where Native American ancestry was associated with lower IgE levels (P = 4.95 × 10(-8)). All but 22q13.1 were replicated in an independent sample of Latinos, and 2 of the peaks were replicated in African Americans (6p21.32-p22.1 and 14q23.2). Fine mapping of 6p21.32-p22.1 identified 6 genome-wide significant single nucleotide polymorphisms in Latinos, 2 of which replicated in European Americans. Another single nucleotide polymorphism was peak-wide significant within 14q23.2 in African Americans (rs1741099, P = 3.7 × 10(-6)) and replicated in non-African American samples (P = .011). CONCLUSION: We confirmed genetic associations at 6 genes and identified novel associations within ZNF365, HLA-DQA1, and 14q23.2. Our results highlight the importance of studying diverse multiethnic populations to uncover novel loci associated with total IgE levels.

selscan: an efficient multithreaded program to perform EHH-based scans for positive selection.

Haplotype-based scans to detect natural selection are useful to identify recent or ongoing positive selection in genomes. As both real and simulated genomic data sets grow larger, spanning thousands of samples and millions of markers, there is a need for a fast and efficient implementation of these scans for general use. Here, we present selscan, an efficient multithreaded application that implements Extended Haplotype Homozygosity (EHH), Integrated Haplotype Score (iHS), and Cross-population EHH (XPEHH). selscan accepts phased genotypes in multiple formats, including TPED, and performs extremely well on both simulated and real data and over an order of magnitude faster than existing available implementations. It calculates iHS on chromosome 22 (22,147 loci) across 204 CEU haplotypes in 353 s on one thread (33 s on 16 threads) and calculates XPEHH for the same data relative to 210 YRI haplotypes in 578 s on one thread (52 s on 16 threads). Source code and binaries (Windows, OSX, and Linux) are available at https://github.com/szpiech/selscan.