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Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of variants that contribute to the genetic component of complex trait variability, these data have brought forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Optimal realization of these objectives requires ongoing methodological developments, designed to identify true trait-associated variants and accurately predict phenotype from genotype. In addition to optimal accuracy, these methods must also be computationally efficient, in order to remain tractable in the context of high variant densities and very large sample sizes. Here we show that the power of linear mixed models that are in widespread use for GWAS can be increased significantly by modeling off target genetic effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Applying this method to simulated and real data we found that it can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data.

Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes. C.S. and S.C. are funded by Science Foundation Ireland grant number 16/IA/4612. peer-reviewed

Background Tumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB. Methods We used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth. Results We found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity. Conclusion Our results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.

Genomics is revolutionizing biomedical research, medicine and healthcare globally in academic, public and industry sectors alike. Concrete examples around the world show that huge benefits for patients, society and economy can be accrued through effective and responsible genomic research and clinical applications. Unfortunately, Ireland has fallen behind and needs to act now in order to catch up. Here, we identify key issues that have resulted in Ireland lagging behind, describe how genomics can benefit Ireland and its people and outline the measures needed to make genomics work for Ireland and Irish patients. There is now an urgent need for a national genomics strategy that enables an effective, collaborative, responsible, well-regulated, and patient centred environment where genome research and clinical genomics can thrive. We present eight recommendations that could be the pillars of a national genomics health strategy.

Motivation: Somatic mutations can have critical prognostic and therapeutic implications for cancer patients. Although targeted methods are often used to assay specific cancer driver mutations, high throughput sequencing is frequently applied to discover novel driver mutations and to determine the status of less-frequent driver mutations. The task of recovering somatic mutations from these data is nontrivial as somatic mutations must be distinguished from germline variants, sequencing errors, and other artefacts. Consequently, bioinformatics pipelines for recovery of somatic mutations from high throughput sequencing typically involve a large number of analytical choices in the form of quality filters. Results: We present vcfView, an interactive tool designed to support the evaluation of somatic mutation calls from cancer sequencing data. The tool takes as input a single variant call format (VCF) file and enables researchers to explore the impacts of analytical choices on the mutant allele frequency spectrum, on mutational signatures and on annotated somatic variants in genes of interest. It allows variants that have failed variant caller filters to be re-examined to improve sensitivity or guide the design of future experiments. It is extensible, allowing other algorithms to be incorporated easily. Availability: The shiny application can be downloaded from GitHub ( https://github.com/BrianOSullivanGit/vcfView ). All data processing is performed within R to ensure platform independence. The app has been tested on RStudio, version 1.1.456, with base R 3.6.2 and Shiny 1.4.0. A vignette based on a publicly available data set is also available on GitHub.

Phylogenetic models of the evolution of protein-coding sequences can provide insights into the selection pressures that have shaped them. In the application of these models synonymous nucleotide substitutions, which do not alter the encoded amino acid, are often assumed to have limited functional consequences and used as a proxy for the neutral rate of evolution. The ratio of nonsynonymous to synonymous substitution rates is then used to categorize the selective regime that applies to the protein (e.g. purifying selection, neutral evolution, diversifying selection). Here, we extend these models to explore the extent of purifying selection acting on substitutions between synonymous stop codons. Using a large collection of coding sequence alignments we estimate that a high proportion (approximately 57%) of mammalian genes are affected by selection acting on stop codon preference. This proportion varies substantially by codon, with UGA stop codons far more likely to be conserved. Genes with evidence of selection acting on synonymous stop codons have distinctive characteristics, compared to unconserved genes with the same stop codon, including enrichment for specific functional properties. Notably, genes with conserved stop codons have longer 3′ UTRs and are associated with shorter mRNA half-life than other genes. The coding regions of these genes are also much more likely to be under strong purifying selection pressure. Our results suggest that the preference for UGA stop codons in many multicellular eukaryotes is selective rather than mutational in origin. Author summary Three codons can specify the termination of translation of protein-coding genes, but the efficiency of translation termination differs between them. Readthrough translation, in which the ribosome fails to terminate at the stop codon, can result in aberrant proteins but can also have a regulatory role. By extending statistical models used to study the evolution of sense codons, we performed a large-scale analysis of selection acting on mutations between the three stop codons in mammals. We estimate that more than half of mammalian protein-coding genes are affected by selection that reduces the rate of substitution between synonymous stop codons, compared to what we would expect if the stop codons were functionally equivalent. Our results suggest that the choice of stop codon is affected by evolutionary selection and impacts the function or regulation of a substantial proportion of mammalian genes.

MHC molecules are capable of presenting neoantigens resulting from somatic mutations on cell surfaces, potentially directing immune responses against cancer. This led to the hypothesis that cancer driver mutations may occur in gaps in the capacity to present neoantigens that are dependent on MHC genotype. If this is correct, it may help to predict the driver mutations that are likely to be observed in an individual from genotype data. Two papers supporting this hypothesis were published in Cell, reporting that driver mutations that occur frequently tend to be poorly presented by common MHC alleles and proposing that MHC genotype is predictive of the driver mutations observed in a patient. Here we demonstrate that these results are a consequence of unjustified statistical assumptions and that the data on which they are based do not provide any evidence of an effect of MHC genotype on the oncogenic mutation landscape.

Summary DNA replication initiates from multiple origins, and selective CDC7 kinase inhibitors (CDC7is) restrain cell proliferation by limiting origin firing. We have performed a CRISPR-Cas9 genome-wide screen to identify genes that, when lost, promote the proliferation of cells treated with sub-efficacious doses of a CDC7i. We have found that the loss of function of ETAA1, an ATR activator, and RIF1 reduce the sensitivity to CDC7is by allowing DNA synthesis to occur more efficiently, notably during late S phase. We show that partial CDC7 inhibition induces ATR mainly through ETAA1, and that if ATR is subsequently inhibited, origin firing is unleashed in a CDK- and CDC7-dependent manner. Cells are then driven into a premature and highly defective mitosis, a phenotype that can be recapitulated by ETAA1 and TOPBP1 co-depletion. This work defines how ATR mediates the effects of CDC7 inhibition, establishing the framework to understand how the origin firing checkpoint functions.

Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we have demonstrated a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response. Electronic supplementary material The online version of this article (10.1186/s13059-018-1507-0) contains supplementary material, which is available to authorized users.

Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we demonstrate a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one-third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response.

Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as "Seshat: Global History Databank." We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history.

Cell subtype proportion variability between samples contributes significantly to the variation of functional genomic properties such as gene expression or DNA methylation. Although the impact of the variation of cell subtype composition on measured genomic quantities is recognized, and some innovative tools have been developed for the analysis of heterogeneous samples, most functional genomics studies using samples with mixed cell types still ignore the influence of cell subtype proportion variation, or just deal with it as a nuisance variable to be eliminated. Here we demonstrate how harvesting information about cell subtype proportions from functional genomics data can provide insights into cellular changes associated with phenotypes. We focused on two types of mixed cell populations, human blood and mouse kidney. Cell type prediction is well developed in the former, but not currently in the latter. Estimating the cellular repertoire is easier when a reference dataset from purified samples of all cell types in the tissue is available, as is the case for blood. However, reference datasets are not available for most other tissues, such as the kidney. In this study, we showed that the proportion of alterations attributable to changes in the cellular composition varies strikingly in the two disorders (asthma and systemic lupus erythematosus), suggesting that the contribution of cell subtype proportion changes to functional genomic properties can be disease-specific. We also showed that a reference dataset from a single-cell RNA-seq study successfully estimated the cell subtype proportions in mouse kidney and allowed us to distinguish altered cell subtype differences between two different knock-out mouse models, both of which had reported a reduced number of glomeruli compared to their wild-type counterparts. These findings demonstrate that testing for changes in cell subtype proportions between conditions can yield important insights in functional genomics studies.

Oligodendrocyte gene expression is downregulated in stress-related neuropsychiatric disorders, including depression. In mice, chronic social stress (CSS) leads to depression-relevant changes in brain and emotional behavior, and the present study shows the involvement of oligodendrocytes in this model. In C57BL/6 (BL/6) mice, RNA-sequencing (RNA-Seq) was conducted with prefrontal cortex, amygdala and hippocampus from CSS and controls; a gene enrichment database for neurons, astrocytes and oligodendrocytes was used to identify cell origin of deregulated genes, and cell deconvolution was applied. To assess the potential causal contribution of reduced oligodendrocyte gene expression to CSS effects, mice heterozygous for the oligodendrocyte gene cyclic nucleotide phosphodiesterase (Cnp1) on a BL/6 background were studied; a 2 genotype (wildtype, Cnp1+/− ) × 2 environment (control, CSS) design was used to investigate effects on emotional behavior and amygdala microglia. In BL/6 mice, in prefrontal cortex and amygdala tissue comprising gray and white matter, CSS downregulated expression of multiple oligodendroycte genes encoding myelin and myelin-axon-integrity proteins, and cell deconvolution identified a lower proportion of oligodendrocytes in amygdala. Quantification of oligodendrocyte proteins in amygdala gray matter did not yield evidence for reduced translation, suggesting that CSS impacts primarily on white matter oligodendrocytes or the myelin transcriptome. In Cnp1 mice, social interaction was reduced by CSS in Cnp1+/− mice specifically; using ionized calcium-binding adaptor molecule 1 (IBA1) expression, microglia activity was increased additively by Cnp1+/− and CSS in amygdala gray and white matter. This study provides back-translational evidence that oligodendrocyte changes are relevant to the pathophysiology and potentially the treatment of stress-related neuropsychiatric disorders.

Aberrant DNA methylation patterns have been reported in inflamed tissues and may play a role in disease. We studied DNA methylation and gene expression profiles of purified intestinal epithelial cells from ulcerative colitis patients, comparing inflamed and non-inflamed areas of the colon. We identified 577 differentially methylated sites (false discovery rate

Yu, Y., R. Ceredig, and C. Seoighe. 2017. A database of human immune receptor alleles recovered from population sequencing data. J . Immunol . 198: [2202–2210][1]. The affiliation for the second author was incorrect as published. The corrected affiliation footnote is shown below. The author line

We share the view of Dr. Watson and colleagues on the importance of germline databases (GLDB) of immune receptor genes and thank them for their interest in our work ([1][1]). Whereas we fully agree with them on many of the caveats that apply to alleles inferred from genome sequence data (discussed

Longitudinal allele frequency data are becoming increasingly prevalent. Such samples permit statistical inference of the population genetics parameters that influence the fate of mutant variants. To infer these parameters by maximum likelihood, the mutant frequency is often assumed to evolve according to the Wright–Fisher model. For computational reasons, this discrete model is commonly approximated by a diffusion process that requires the assumption that the forces of natural selection and mutation are weak. This assumption is not always appropriate. For example, mutations that impart drug resistance in pathogens may evolve under strong selective pressure. Here, we present an alternative approximation to the mutant-frequency distribution that does not make any assumptions about the magnitude of selection or mutation and is much more computationally efficient than the standard diffusion approximation. Simulation studies are used to compare the performance of our method to that of the Wright–Fisher and Gaussian diffusion approximations. For large populations, our method is found to provide a much better approximation to the mutant-frequency distribution when selection is strong, while all three methods perform comparably when selection is weak. Importantly, maximum-likelihood estimates of the selection coefficient are severely attenuated when selection is strong under the two diffusion models, but not when our method is used. This is further demonstrated with an application to mutant-frequency data from an experimental study of bacteriophage evolution. We therefore recommend our method for estimating the selection coefficient when the effective population size is too large to utilize the discrete Wright–Fisher model.

Objective(s): There is limited information on full-length genome sequences and the early evolution of transmitted HIV-1 subtype C viruses, which constitute the majority of viruses spread in Africa. The purpose of this study was to characterize the earliest changes across the genome of subtype C viruses following transmission, to better understand early control of viremia. Design: We derived the near full-length genome sequence responsible for clinical infection from five HIV subtype C-infected individuals with different disease progression profiles and tracked adaptation to immune responses in the first 6 months of infection. Methods: Near full-length genomes were generated by single genome amplification and direct sequencing. Sequences were analyzed for amino acid mutations associated with cytotoxic T lymphocyte (CTL) or antibody-mediated immune pressure, and for reversion. Results: Fifty-five sequence changes associated with adaptation to the new host were identified, with 38% attributed to CTL pressure, 35% to antibody pressure, 16% to reversions and the remainder were unclassified. Mutations in CTL epitopes were most frequent in thefirst 5 weeks of infection, with the frequency declining over time with the decline in viral load. CTL escape predominantly occurred in nef, followed by pol and env. Shuffling/toggling of mutations was identified in 81% of CTL epitopes, with only 7% reaching fixation within the 6-month period. Conclusion: There was rapid virus adaptation following transmission, predominantly driven by CTL pressure, with most changes occurring during high viremia. Rapid escape and complex escape pathways provide further challenges for vaccine protection. 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins AIDS 2013, 27:507‐518

Nonnegative Matrix Factorization (NMF) has proved to be an effective method for unsupervised clustering analysis of gene expression data. By the nonnegativity constraint, NMF provides a decomposition of the data matrix into two matrices that have been used for clustering analysis. However, the decomposition is not unique. This allows different clustering results to be obtained, resulting in different interpretations of the decomposition. To alleviate this problem, some existing methods directly enforce uniqueness to some extent by adding regularization terms in the NMF objective function. Alternatively, various normalization methods have been applied to the factor matrices; however, the effects of the choice of normalization have not been carefully investigated. Here we investigate the performance of NMF for the task of cancer class discovery, under a wide range of normalization choices. After extensive evaluations, we observe that the maximum norm showed the best performance, although the maximum norm has not previously been used for NMF. Matlab codes are freely available from: http://maths.nuigalway.ie/~haixuanyang/pNMF/pNMF.htm.

Although they do not contribute directly to the proteome, introns frequently contain regulatory elements and can extend the protein coding potential of the genome through alternative splicing. For some genes, the contribution of introns to the time required for transcription can also be functionally significant. We have previously shown that intron length in genes associated with developmental patterning is often highly conserved. In general, sets of genes that require precise coordination in the timing of their expression may be sensitive to changes in transcript length. A prediction of this hypothesis is that evolutionary changes in intron length, when they occur, may be correlated between sets of coordinately expressed genes. To test this hypothesis, we analyzed intron length coevolution in alignments from nine eutherian mammals. Overall, genes that belong to the same protein complex or that are coexpressed were significantly more likely to show evidence of intron length coevolution than matched, randomly sampled genes. Individually, protein complexes involved in the cell cycle showed the strongest evidence of coevolution of intron lengths and clusters of coexpressed genes enriched for cell cycle genes also showed significant evidence of intron length coevolution. Our results reveal a novel aspect of gene coevolution and provide a means to identify genes, protein complexes and biological processes that may be particularly sensitive to changes in transcriptional dynamics.

Region and cell-type specific differences in the molecular make up of colon epithelial cells have been reported. Those differences may underlie the region-specific characteristics of common colon epithelial diseases such as colorectal cancer and inflammatory bowel disease. DNA methylation is a cell-type specific epigenetic mark, essential for transcriptional regulation, silencing of repetitive DNA and genomic imprinting. Little is known about any region-specific variations in methylation patterns in human colon epithelial cells. Using purified epithelial cells and whole biopsies ( n = 19) from human subjects, we generated epigenome-wide DNA methylation data (using the HELP-tagging assay), comparing the methylation signatures of the proximal and distal colon. We identified a total of 125 differentially methylated sites (DMS) mapping to transcription start sites of protein-coding genes, most notably several members of the homeobox ( HOX) family of genes. Patterns of differential methylation were validated with MassArray EpiTYPER. We also examined DNA methylation in whole biopsies, applying a computational technique to deconvolve variation in methylation within cell types and variation in cell-type composition across biopsies. Including inferred epithelial proportions as a covariate in differential methylation analysis applied to the whole biopsies resulted in greater overlap with the results obtained from purified epithelial cells compared with when the covariate was not included. Results obtained from both approaches highlight region-specific methylation patterns of HOX genes in colonic epithelium. Regional variation in methylation patterns has implications for the study of diseases that exhibit regional expression patterns in the human colon, such as inflammatory bowel disease and colorectal cancer.

Flowering plants, angiosperms, can be divided into two major clades, monocots and dicots, and while differences in amino acid composition in different species from the two clades have been reported, a systematic analysis of amino acid content and distribution remains outstanding. Here, we show that monocot and dicot proteins have developed distinct amino acid content. In Arabidopsis thaliana and poplar, as in the ancestral moss Physcomitrella patens, the average mass per amino acid appears to be independent of protein length, while in the monocots rice, maize and sorghum, shorter proteins tend to be made of lighter amino acids. An examination of the elemental content of these proteomes reveals that the difference between monocot and dicot proteins can be largely attributed to their different carbon signatures. In monocots, the shorter proteins, which comprise the majority of all proteins, are made of amino acids with less carbon, while the nitrogen content is unchanged in both monocots and dicots. We hypothesise that this signature could be the result of carbon use and energy optimisation in fast-growing annual Poaceae (grasses).

Genome-wide association studies (GWAS) have identified hundreds of inherited genetic variants that increase cancer risk. However, most cancer risk variants are in non-coding regions of the genome and modulate risk by affecting gene regulation. Thus, determining how inherited genetic variation affects gene expression in cancer is critically important to understanding disease development. Consequently, by leveraging large genomics datasets like The Cancer Genome Atlas (TCGA), previous studies have examined the effect of inherited genetic variation on gene expression in tumors; an approach known as expression quantitative trait locus (eQTL) mapping. However, tumors are mixtures of both cancer and normal cells, for example, immune cells and stroma. No previous cancer eQTL study has accounted for this mixture of cell types, essentially having treated bulk tumor expression as representative of gene expression in cancer cells. Building on research in deconvolution of mixtures of cell types, we have developed a new approach that can accurately account for the effect of tumor-infiltrating normal cells on cancer eQTLs. The approach involves first estimating the proportion of tumor-infiltrating normal cells (tumor purity) using a combined estimate from genomics data and H&E staining. Then, we developed a statistical model that can account for the effect of tumor purity on eQTLs by modeling the interaction of the tumor purity estimate and genotype. Intuitively, this works by determining how the magnitude of the association between gene expression and genotype changes as a function of tumor purity, then extrapolating this effect to 100% cancer cells. The model's accuracy was validated using simulated data and on expression profiles from purified cell types. We used this model to map eQTLs in the TCGA breast cancer cohort. Remarkably, while 57,189 eQTLs were identified in TCGA breast cancer patients using a conventional model, only 8,833 (15.4%) could be attributed to cancer cells when tumor purity was accounted for. Analysis of the Genotype-Tissue Expression (GTEx) data provided evidence that almost 50% of tumor eQTLs inferred using a conventional approach affect gene expression in tumor-infiltrating immune cells and fibroblasts. We also investigated the eQTL profiles of cancer risk variants from a GWAS for breast cancer risk. Strikingly, for 33% of breast cancer risk variants identified as eQTLs using a conventional approach, we found strong evidence of an effect in tumor-infiltrating normal cells, but no evidence of an effect in cancer cells. This suggests cancer risk is mediated by the effect of inherited genetic variation on gene regulation in the cells of the tumor microenvironment, as well as in cancer and pre-cancer cells. Our findings profoundly challenge the current interpretation of inherited genetic regulation in cancer and should be considered in functional validation of results from all cancer risk GWAS. Citation Format: Paul Geeleher, Fan Wang, Zheny Zhang, Robert L. Grossman, Cathal Seoighe, R. Stephanie Huang. Most expression quantitative trait loci discovered in tumors cannot be attributed to cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4271.

Admixed populations present unique opportunities to discover the genetic factors underlying many multifactorial diseases. The geographical position and complex history of South Africa has led to the establishment of the unique admixed population known as the South African Coloured. Not much is known about the genetic make-up of this population, and the historical record is patchy. We genotyped 959 individuals from the Western Cape area, self-identified as belonging to this population, using the Affymetrix 500k genotyping platform. This resulted in nearly 75,000 autosomal SNPs that could be compared with populations represented in the International HapMap Project and the Human Genome Diversity Project. Analysis by means of both the admixture and linkage models in STRUCTURE revealed that the major ancestral components of this population are predominantly Khoesan (32-43%), Bantu-speaking Africans (20-36%), European (21-28%) and a smaller Asian contribution (9-11%), depending on the model used. This is consistent with historical data. While of great historical and genealogical interest, this information is also essential for future admixture mapping of disease genes in this population.

Existing methods for the prediction of immunologically active T-cell epitopes are based on the amino acid sequence or structure of pathogen proteins. Additional information regarding the locations of epitopes may be acquired by considering the evolution of viruses in hosts with different immune backgrounds. In particular, immune-dependent evolutionary patterns at sites within or near T-cell epitopes can be used to enhance epitope identification. We have developed a mutation-selection model of T-cell epitope evolution that allows the human leukocyte antigen (HLA) genotype of the host to influence the evolutionary process. This is one of the first examples of the incorporation of environmental parameters into a phylogenetic model and has many other potential applications where the selection pressures exerted on an organism can be related directly to environmental factors. We combine this novel evolutionary model with a hidden Markov model to identify contiguous amino acid positions that appear to evolve under immune pressure in the presence of specific host immune alleles and that therefore represent potential epitopes. This phylogenetic hidden Markov model provides a rigorous probabilistic framework that can be combined with sequence or structural information to improve epitope prediction. As a demonstration, we apply the model to a data set of HIV-1 protein-coding sequences and host HLA genotypes.

It is unresolved whether recently transmitted human immunodeficiency viruses (HIV) have genetic features that specifically favour their transmissibility. To identify potential “transmission signatures”, we compared 20 full-length HIV-1 subtype C genomes from primary infections, with 66 sampled from ethnically and geographically matched individuals with chronic infections. Controlling for recombination and phylogenetic relatedness, we identified 39 sites at which amino acid frequency spectra differed significantly between groups. These sites were predominantly located within Env, Pol and Gag (14/39, 9/39 and 6/39 respectively) and were significantly clustered (33/39) within known immunoreactive peptides. Within 6 months of infection, we detected reversion-to-consensus mutations at 14 sites and potential CTL escape mutations at seven. Here we provide evidence that frequent reversion mutations probably allows the virus to recover replicative fitness which, together with immune escape driven by the HLA alleles of the new hosts, differentiate sequences from chronic infections from those sampled shortly after transmission.