Uganda Genome Resource Enables Insights into Population History and Genomic Discovery in Africa.

Genomic studies in African populations provide unique opportunities to understand disease etiology, human diversity, and population history. In the largest study of its kind, comprising genome-wide data from 6,400 individuals and whole-genome sequences from 1,978 individuals from rural Uganda, we find evidence of geographically correlated fine-scale population substructure. Historically, the ancestry of modern Ugandans was best represented by a mixture of ancient East African pastoralists. We demonstrate the value of the largest sequence panel from Africa to date as an imputation resource. Examining 34 cardiometabolic traits, we show systematic differences in trait heritability between European and African populations, probably reflecting the differential impact of genes and environment. In a multi-trait pan-African GWAS of up to 14,126 individuals, we identify novel loci associated with anthropometric, hematological, lipid, and glycemic traits. We find that several functionally important signals are driven by Africa-specific variants, highlighting the value of studying diverse populations across the region.

Linear mixed model for heritability estimation that explicitly addresses environmental variation.

The linear mixed model (LMM) is now routinely used to estimate heritability. Unfortunately, as we demonstrate, LMM estimates of heritability can be inflated when using a standard model. To help reduce this inflation, we used a more general LMM with two random effects-one based on genomic variants and one based on easily measured spatial location as a proxy for environmental effects. We investigated this approach with simulated data and with data from a Uganda cohort of 4,778 individuals for 34 phenotypes including anthropometric indices, blood factors, glycemic control, blood pressure, lipid tests, and liver function tests. For the genomic random effect, we used identity-by-descent estimates from accurately phased genome-wide data. For the environmental random effect, we constructed a covariance matrix based on a Gaussian radial basis function. Across the simulated and Ugandan data, narrow-sense heritability estimates were lower using the more general model. Thus, our approach addresses, in part, the issue of "missing heritability" in the sense that much of the heritability previously thought to be missing was fictional. Software is available at https://github.com/MicrosoftGenomics/FaST-LMM.

Further improvements to linear mixed models for genome-wide association studies.

We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science.

HIV control is mediated in part by CD8+ T-cell targeting of specific epitopes.

UNLABELLED: We investigated the hypothesis that the correlation between the class I HLA types of an individual and whether that individual spontaneously controls HIV-1 is mediated by the targeting of specific epitopes by CD8(+) T cells. By measuring gamma interferon enzyme-linked immunosorbent spot (ELISPOT) assay responses to a panel of 257 optimally defined epitopes in 341 untreated HIV-infected persons, including persons who spontaneously control viremia, we found that the correlation between HLA types and control is mediated by the targeting of specific epitopes. Moreover, we performed a graphical model-based analysis that suggested that the targeting of specific epitopes is a cause of such control--that is, some epitopes are protective rather than merely associated with control--and identified eight epitopes that are significantly protective. In addition, we use an in silico analysis to identify protein regions where mutations are likely to affect the stability of a protein, and we found that the protective epitopes identified by the ELISPOT analysis correspond almost perfectly to such regions. This in silico analysis thus suggests a possible mechanism for control and could be used to identify protective epitopes that are not often targeted in natural infection but that may be potentially useful in a vaccine. Our analyses thus argue for the inclusion (and exclusion) of specific epitopes in an HIV vaccine. IMPORTANCE: Some individuals naturally control HIV replication in the absence of antiretroviral therapy, and this ability to control is strongly correlated with the HLA class I alleles that they express. Here, in a large-scale experimental study, we provide evidence that this correlation is mediated largely by the targeting of specific CD8(+) T-cell epitopes, and we identify eight epitopes that are likely to cause control. In addition, we provide an in silico analysis indicating that control occurs because mutations within these epitopes change the stability of the protein structures. This in silico analysis also identified additional epitopes that are not typically targeted in natural infection but may lead to control when included in a vaccine, provided that other epitopes that would otherwise distract the immune system from targeting them are excluded from the vaccine.

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants.

MOTIVATION: Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test-a score test-with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene-gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods. RESULTS: After reviewing theoretical differences in performance between the score and LR tests, we find empirically on real data that the LR test generally has more power. In particular, on 15 of 17 real datasets, the LR test yielded at least as many associations as the score test-up to 23 more associations-whereas the score test yielded at most one more association than the LR test in the two remaining datasets. On synthetic data, we find that the LR test yielded up to 12% more associations, consistent with our results on real data, but also observe a regime of extremely small signal where the score test yielded up to 25% more associations than the LR test, consistent with theory. Finally, our computational speedups now enable (i) efficient LR testing when the background kernel is full rank, and (ii) efficient score testing when the background kernel changes with each test, as for gene-gene interaction tests. The latter yielded a factor of 2000 speedup on a cohort of size 13 500. AVAILABILITY: Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/. CONTACT: heckerma@microsoft.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The benefits of selecting phenotype-specific variants for applications of mixed models in genomics.

Applications of linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of variants (e.g., rare variants) for association. In each of these applications, the LMM uses a genetic similarity matrix, which encodes the pairwise similarity between every two individuals in a cohort. Although ideally these similarities would be estimated using strictly variants relevant to the given phenotype, the identity of such variants is typically unknown. Consequently, relevant variants are excluded and irrelevant variants are included, both having deleterious effects. For each application of the LMM, we review known effects and describe new effects showing how variable selection can be used to mitigate them.

A powerful and efficient set test for genetic markers that handles confounders.

MOTIVATION: Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set tests allow for aggregation of weak signal within a set, can capture interplay among variants and reduce the burden of multiple hypothesis testing. Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger datasets are used to increase power. RESULTS: We introduce a new approach for set tests that handles confounders. Our model is based on the linear mixed model and uses two random effects-one to capture the set association signal and one to capture confounders. We also introduce a computational speedup for two random-effects models that makes this approach feasible even for extremely large cohorts. Using this model with both the likelihood ratio test and score test, we find that the former yields more power while controlling type I error. Application of our approach to richly structured Genetic Analysis Workshop 14 data demonstrates that our method successfully corrects for population structure and family relatedness, whereas application of our method to a 15 000 individual Crohn's disease case-control cohort demonstrates that it additionally recovers genes not recoverable by univariate analysis. AVAILABILITY: A Python-based library implementing our approach is available at http://mscompbio.codeplex.com.

An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data.

We present an approach for genome-wide association analysis with improved power on the Wellcome Trust data consisting of seven common phenotypes and shared controls. We achieved improved power by expanding the control set to include other disease cohorts, multiple races, and closely related individuals. Within this setting, we conducted exhaustive univariate and epistatic interaction association analyses. Use of the expanded control set identified more known associations with Crohn's disease and potential new biology, including several plausible epistatic interactions in several diseases. Our work suggests that carefully combining data from large repositories could reveal many new biological insights through increased power. As a community resource, all results have been made available through an interactive web server.

High-dimensional immunomonitoring models of HIV-1-specific CD8 T-cell responses accurately identify subjects achieving spontaneous viral control.

UNLABELLED: The development of immunomonitoring models to determine HIV-1 vaccine efficacy is a major challenge. Studies suggest that HIV-1­specific CD8 T cells play a critical role in subjects achieving spontaneous viral control (HIV-1 controllers) and that they will be important in immune interventions. However, no single CD8 T-cell function is uniquely associated with controller status and the heterogeneity of responses targeting different epitopes further complicates the discovery of determinants of protective immunity. In the present study, we describe immunomonitoring models integrating multiple functions of epitope-specific CD8 T cells that distinguish controllers from subjects with treated or untreated progressive infection. Models integrating higher numbers of variables and trained with the least absolute shrinkage and selection operator (LASSO) variant of logistic regression and 10-fold cross-validation produce "diagnostic tests" that display an excellent capacity to delineate subject categories. The test accuracy reaches 75% area under the receiving operating characteristic curve in cohorts matched for prevalence of protective alleles. Linear mixed-effects model analyses show that the proliferative capacity, cytokine production, and kinetics of cytokine secretion are associated with HIV-1 control. Although proliferative capacity is the strongest single discriminant, integrated modeling of different dimensions of data leverages individual associations. This strategy may have important applications in predictive model development and immune monitoring of HIV-1 vaccine trials. KEY POINTS: Immune monitoring models integrating multiple functions of HIV-1-specific CD8 T cells distinguish controllers from subjects with progressive HIV-1 infection. This strategy may have important applications in predictive model development and immune monitoring of HIV-1 vaccine trials.

Fine-mapping classical HLA variation associated with durable host control of HIV-1 infection in African Americans.

A small proportion of human immunodeficiency virus-1 (HIV-1) infected individuals, termed HIV-1 controllers, suppress viral replication to very low levels in the absence of therapy. Genetic investigations of this phenotype have strongly implicated variation in the class I major histocompatibility complex (MHC) region as key to HIV-1 control. We collected sequence-based classical class I HLA genotypes at 4-digit resolution in HIV-1-infected African American controllers and progressors (n = 1107), and tested them for association with host control using genome-wide single nucleotide polymorphism data to account for population structure. Several classical alleles at HLA-B were associated with host control, including B*57:03 [odds ratio (OR) = 5.1; P= 3.4 × 10(-18)] and B*81:01 (OR = 4.8; P= 1.3 × 10(-9)). Analysis of variable amino acid positions demonstrates that HLA-B position 97 is the most significant association with host control in African Americans (omnibus P = 1.2 × 10(-21)) and explains the signal of several HLA-B alleles, including B*57:03. Within HLA-B, we also identified independent effects at position 116 (omnibus P= 2.8 × 10(-15)) in the canonical F pocket, position 63 in the B pocket (P= 1.5 × 10(-3)) and the non-pocket position 245 (P= 8.8 × 10(-10)), which is thought to influence CD8-binding kinetics. Adjusting for these HLA-B effects, there is evidence for residual association in the MHC region. These results underscore the key role of HLA-B in affecting HIV-1 replication, likely through the molecular interaction between HLA-B and viral peptides presented by infected cells, and suggest that sites outside the peptide-binding pocket also influence HIV-1 control.

Learning transcriptional regulatory relationships using sparse graphical models.

Understanding the organization and function of transcriptional regulatory networks by analyzing high-throughput gene expression profiles is a key problem in computational biology. The challenges in this work are 1) the lack of complete knowledge of the regulatory relationship between the regulators and the associated genes, 2) the potential for spurious associations due to confounding factors, and 3) the number of parameters to learn is usually larger than the number of available microarray experiments. We present a sparse (L1 regularized) graphical model to address these challenges. Our model incorporates known transcription factors and introduces hidden variables to represent possible unknown transcription and confounding factors. The expression level of a gene is modeled as a linear combination of the expression levels of known transcription factors and hidden factors. Using gene expression data covering 39,296 oligonucleotide probes from 1109 human liver samples, we demonstrate that our model better predicts out-of-sample data than a model with no hidden variables. We also show that some of the gene sets associated with hidden variables are strongly correlated with Gene Ontology categories. The software including source code is available at http://grnl1.codeplex.com.

Widespread impact of HLA restriction on immune control and escape pathways of HIV-1.

The promiscuous presentation of epitopes by similar HLA class I alleles holds promise for a universal T-cell-based HIV-1 vaccine. However, in some instances, cytotoxic T lymphocytes (CTL) restricted by HLA alleles with similar or identical binding motifs are known to target epitopes at different frequencies, with different functional avidities and with different apparent clinical outcomes. Such differences may be illuminated by the association of similar HLA alleles with distinctive escape pathways. Using a novel computational method featuring phylogenetically corrected odds ratios, we systematically analyzed differential patterns of immune escape across all optimally defined epitopes in Gag, Pol, and Nef in 2,126 HIV-1 clade C-infected adults. Overall, we identified 301 polymorphisms in 90 epitopes associated with HLA alleles belonging to shared supertypes. We detected differential escape in 37 of 38 epitopes restricted by more than one allele, which included 278 instances of differential escape at the polymorphism level. The majority (66 to 97%) of these resulted from the selection of unique HLA-specific polymorphisms rather than differential epitope targeting rates, as confirmed by gamma interferon (IFN-γ) enzyme-linked immunosorbent spot assay (ELISPOT) data. Discordant associations between HLA alleles and viral load were frequently observed between allele pairs that selected for differential escape. Furthermore, the total number of associated polymorphisms strongly correlated with average viral load. These studies confirm that differential escape is a widespread phenomenon and may be the norm when two alleles present the same epitope. Given the clinical correlates of immune escape, such heterogeneity suggests that certain epitopes will lead to discordant outcomes if applied universally in a vaccine.

FaST linear mixed models for genome-wide association studies.

We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).

HIV-1 adaptation to NK-cell-mediated immune pressure.

Natural killer (NK) cells have an important role in the control of viral infections, recognizing virally infected cells through a variety of activating and inhibitory receptors. Epidemiological and functional studies have recently suggested that NK cells can also contribute to the control of HIV-1 infection through recognition of virally infected cells by both activating and inhibitory killer immunoglobulin-like receptors (KIRs). However, it remains unknown whether NK cells can directly mediate antiviral immune pressure in vivo in humans. Here we describe KIR-associated amino-acid polymorphisms in the HIV-1 sequence of chronically infected individuals, on a population level. We show that these KIR-associated HIV-1 sequence polymorphisms can enhance the binding of inhibitory KIRs to HIV-1-infected CD4(+) T cells, and reduce the antiviral activity of KIR-positive NK cells. These data demonstrate that KIR-positive NK cells can place immunological pressure on HIV-1, and that the virus can evade such NK-cell-mediated immune pressure by selecting for sequence polymorphisms, as was previously described for virus-specific T cells and neutralizing antibodies. NK cells might therefore have a previously underappreciated role in contributing to viral evolution.

Translation of HLA-HIV associations to the cellular level: HIV adapts to inflate CD8 T cell responses against Nef and HLA-adapted variant epitopes.

Strong statistical associations between polymorphisms in HIV-1 population sequences and carriage of HLA class I alleles have been widely used to identify possible sites of CD8 T cell immune selection in vivo. However, there have been few attempts to prospectively and systematically test these genetic hypotheses arising from population-based studies at a cellular, functional level. We assayed CD8 T cell epitope-specific IFN-γ responses in 290 individuals from the same cohort, which gave rise to 874 HLA-HIV associations in genetic analyses, taking into account autologous viral sequences and individual HLA genotypes. We found immunological evidence for 58% of 374 associations tested as sites of primary immune selection and identified up to 50 novel HIV-1 epitopes using this reverse-genomics approach. Many HLA-adapted epitopes elicited equivalent or higher-magnitude IFN-γ responses than did the nonadapted epitopes, particularly in Nef. At a population level, inclusion of all of the immunoreactive variant CD8 T cell epitopes in Gag, Pol, Nef, and Env suggested that HIV adaptation leads to an inflation of Nef-directed immune responses relative to other proteins. We concluded that HLA-HIV associations mark viral epitopes subject to CD8 T cell selection. These results can be used to guide functional studies of specific epitopes and escape mutations, as well as to test, train, and evaluate analytical models of viral escape and fitness. The inflation of Nef and HLA-adapted variant responses may have negative effects on natural and vaccine immunity against HIV and, therefore, has implications for diversity coverage approaches in HIV vaccine design.

Conditional random fields for fast, large-scale genome-wide association studies.

Understanding the role of genetic variation in human diseases remains an important problem to be solved in genomics. An important component of such variation consist of variations at single sites in DNA, or single nucleotide polymorphisms (SNPs). Typically, the problem of associating particular SNPs to phenotypes has been confounded by hidden factors such as the presence of population structure, family structure or cryptic relatedness in the sample of individuals being analyzed. Such confounding factors lead to a large number of spurious associations and missed associations. Various statistical methods have been proposed to account for such confounding factors such as linear mixed-effect models (LMMs) or methods that adjust data based on a principal components analysis (PCA), but these methods either suffer from low power or cease to be tractable for larger numbers of individuals in the sample. Here we present a statistical model for conducting genome-wide association studies (GWAS) that accounts for such confounding factors. Our method scales in runtime quadratic in the number of individuals being studied with only a modest loss in statistical power as compared to LMM-based and PCA-based methods when testing on synthetic data that was generated from a generalized LMM. Applying our method to both real and synthetic human genotype/phenotype data, we demonstrate the ability of our model to correct for confounding factors while requiring significantly less runtime relative to LMMs. We have implemented methods for fitting these models, which are available at http://www.microsoft.com/science.

Variable HIV peptide stability in human cytosol is critical to epitope presentation and immune escape.

Induction of virus-specific CD8⁺ T cell responses is critical for the success of vaccines against chronic viral infections. Despite the large number of potential MHC-I-restricted epitopes located in viral proteins, MHC-I-restricted epitope generation is inefficient, and factors defining the production and presentation of MHC-I-restricted viral epitopes are poorly understood. Here, we have demonstrated that the half-lives of HIV-derived peptides in cytosol from primary human cells were highly variable and sequence dependent, and significantly affected the efficiency of cell recognition by CD8⁺ T cells. Furthermore, multiple clinical isolates of HLA-associated HIV epitope variants displayed reduced half-lives relative to consensus sequence. This decreased cytosolic peptide stability diminished epitope presentation and CTL recognition, illustrating a mechanism of immune escape. Chaperone complexes including Hsp90 and histone deacetylase HDAC6 enhanced peptide stability by transient protection from peptidase degradation. Based on empirical results with 166 peptides, we developed a computational approach utilizing a sequence-based algorithm to estimate the cytosolic stability of antigenic peptides. Our results identify sequence motifs able to alter the amount of peptide available for loading onto MHC-I, suggesting potential new strategies to modulate epitope production from vaccine immunogens.

Reduced replication capacity of NL4-3 recombinant viruses encoding reverse transcriptase-integrase sequences from HIV-1 elite controllers.

BACKGROUND: Identifying viral and host determinants of HIV-1 elite control may help inform novel therapeutic and/or vaccination strategies. Previously, we observed decreased replication capacity in controller-derived viruses suggesting that fitness consequences of human leukocyte antigen (HLA) class I-associated escape mutations in Gag may contribute to this phenotype. This study examines whether similar functional defects occur in Pol proteins of elite controllers. METHODS: Recombinant NL4-3 viruses encoding plasma RNA-derived reverse transcriptase-integrase sequences from 58 elite controllers and 50 untreated chronic progressors were constructed, and replication capacity measured in vitro using a green fluorescent protein (GFP) reporter T-cell assay. Sequences were analyzed for drug resistance and HLA-associated viral polymorphisms. RESULTS: Controller-derived viruses displayed significantly lower replication capacity compared with those from progressors (P < 0.0001). Among controllers, the most attenuated viruses were generated from individuals expressing HLA-B*57 or B*51. In viruses from B*57+ progressors (n = 8), a significant inverse correlation was observed between B*57-associated reverse transcriptase-integrase escape mutations and replication capacity (R = -0.89; P = 0.003); a similar trend was observed in B*57+ controller-derived viruses (n = 20, R = -0.36; P = 0.08). CONCLUSIONS: HIV-1 Pol function seemed to be compromised in elite controllers. As observed previously for Gag, HLA-associated immune pressure in Pol may contribute to viral attenuation and subsequent control of viremia.

The major genetic determinants of HIV-1 control affect HLA class I peptide presentation.

Infectious and inflammatory diseases have repeatedly shown strong genetic associations within the major histocompatibility complex (MHC); however, the basis for these associations remains elusive. To define host genetic effects on the outcome of a chronic viral infection, we performed genome-wide association analysis in a multiethnic cohort of HIV-1 controllers and progressors, and we analyzed the effects of individual amino acids within the classical human leukocyte antigen (HLA) proteins. We identified >300 genome-wide significant single-nucleotide polymorphisms (SNPs) within the MHC and none elsewhere. Specific amino acids in the HLA-B peptide binding groove, as well as an independent HLA-C effect, explain the SNP associations and reconcile both protective and risk HLA alleles. These results implicate the nature of the HLA-viral peptide interaction as the major factor modulating durable control of HIV infection.