HIV peptidome-wide association study reveals patient-specific epitope repertoires associated with HIV control.

Genetic variation in the peptide-binding groove of the highly polymorphic HLA class I molecules has repeatedly been associated with HIV-1 control and progression to AIDS, accounting for up to 12% of the variation in HIV-1 set point viral load (spVL). This suggests a key role in disease control for HLA presentation of HIV-1 epitopes to cytotoxic T cells. However, a comprehensive understanding of the relevant HLA-bound HIV epitopes is still elusive. Here we describe a peptidome-wide association study (PepWAS) approach that integrates HLA genotypes and spVL data from 6,311 HIV-infected patients to interrogate the entire HIV-1 proteome (3,252 unique peptides) for disease-relevant peptides. This PepWAS approach predicts a core set of epitopes associated with spVL, including known epitopes but also several previously uncharacterized disease-relevant peptides. More important, each patient presents only a small subset of these predicted core epitopes through their individual HLA-A and HLA-B variants. Eventually, the individual differences in these patient-specific epitope repertoires account for the variation in spVL that was previously associated with HLA genetic variation. PepWAS thus enables a comprehensive functional interpretation of the robust but little-understood association between HLA and HIV-1 control, prioritizing a short list of disease-associated epitopes for the development of targeted therapy.

Accel-Align: a fast sequence mapper and aligner based on the seed-embed-extend method.

BACKGROUND: Improvements in sequencing technology continue to drive sequencing cost towards $100 per genome. However, mapping sequenced data to a reference genome remains a computationally-intensive task due to the dependence on edit distance for dealing with INDELs and mismatches introduced by sequencing. All modern aligners use seed-filter-extend methodology and rely on filtration heuristics to reduce the overhead of edit distance computation. However, filtering has inherent performance-accuracy trade-offs that limits its effectiveness. RESULTS: Motivated by algorithmic advances in randomized low-distortion embedding, we introduce SEE, a new methodology for developing sequence mappers and aligners. While SFE focuses on eliminating sub-optimal candidates, SEE focuses instead on identifying optimal candidates. To do so, SEE transforms the read and reference strings from edit distance regime to the Hamming regime by embedding them using a randomized algorithm, and uses Hamming distance over the embedded set to identify optimal candidates. To show that SEE performs well in practice, we present Accel-Align an SEE-based short-read sequence mapper and aligner that is 3-12[Formula: see text] faster than state-of-the-art aligners on commodity CPUs, without any special-purpose hardware, while providing comparable accuracy. CONCLUSIONS: As sequencing technologies continue to increase read length while improving throughput and accuracy, we believe that randomized embeddings open up new avenues for optimization that cannot be achieved by using edit distance. Thus, the techniques presented in this paper have a much broader scope as they can be used for other applications like graph alignment, multiple sequence alignment, and sequence assembly.

Whole-exome Sequencing for the Identification of Rare Variants in Primary Immunodeficiency Genes in Children With Sepsis: A Prospective, Population-based Cohort Study.

BACKGROUND: The role of primary immunodeficiencies (PID) in susceptibility to sepsis remains unknown. It is unclear whether children with sepsis benefit from genetic investigations. We hypothesized that sepsis may represent the first manifestation of underlying PID. We applied whole-exome sequencing (WES) to a national cohort of children with sepsis to identify rare, predicted pathogenic variants in PID genes. METHODS: We conducted a multicenter, population-based, prospective study including previously healthy children aged ≥28 days and <17 years admitted with blood culture-proven sepsis. Using a stringent variant filtering procedure, analysis of WES data was restricted to rare, predicted pathogenic variants in 240 PID genes for which increased susceptibility to bacterial infection has been reported. RESULTS: There were 176 children presenting with 185 sepsis episodes who underwent WES (median age, 52 months; interquartile range, 15.4-126.4). There were 41 unique predicted pathogenic PID variants (1 homozygous, 5 hemizygous, and 35 heterozygous) found in 35/176 (20%) patients, including 3/176 (2%) patients carrying variants that were previously reported to lead to PID. The variants occurred in PID genes across all 8 PID categories, as defined by the International Union of Immunological Societies. We did not observe a significant correlation between clinical or laboratory characteristics of patients and the presence or absence of PID variants. CONCLUSIONS: Applying WES to a population-based cohort of previously healthy children with bacterial sepsis detected variants of uncertain significance in PID genes in 1 out of 5 children. Future studies need to investigate the functional relevance of these variants to determine whether variants in PID genes contribute to pediatric sepsis susceptibility.

Human genomics of acute liver failure due to hepatitis B virus infection: An exome sequencing study in liver transplant recipients.

Acute liver failure (ALF) or fulminant hepatitis is a rare, yet severe outcome of infection with hepatitis B virus (HBV) that carries a high mortality rate. The occurrence of a life-threatening condition upon infection with a prevalent virus in individuals without known risk factors is suggestive of pathogen-specific immune dysregulation. In the absence of established differences in HBV virulence, we hypothesized that ALF upon primary infection with HBV could be due to rare deleterious variants in the human genome. To search for such variants, we performed exome sequencing in 21 previously healthy adults who required liver transplantation upon fulminant HBV infection and 172 controls that were positive for anti-HBc and anti-HBs but had no clinical history of jaundice or liver disease. After a series of hypothesis-driven filtering steps, we searched for putatively pathogenic variants that were significantly associated with case-control status. We did not find any causal variant or gene, a result that does not support the hypothesis of a shared monogenic basis for human susceptibility to HBV-related ALF in adults. This study represents a first attempt at deciphering the human genetic contribution to the most severe clinical presentation of acute HBV infection in previously healthy individuals.

A test for detecting differential indirect trans effects between two groups of samples.

Integrative analysis of copy number and gene expression data can help in understanding the cis and trans effect of copy number aberrations on transcription levels of genes involved in a pathway. To analyse how these copy number mediated gene-gene interactions differ between groups of samples we propose a new method, named dNET. Our method uses ridge regression to model the network topology involving one gene's expression level, its gene dosage and the expression levels of other genes in the network. The interaction parameters are estimated by fitting the model per gene for all samples together. However, instead of testing for differential network topology per gene, dNET tests for an overall difference in estimated parameters between two groups of samples and produces a single p-value. With the help of several simulation studies, we show that dNET can detect differential network nodes with high accuracy and low rate of false positives even in the presence of differential cis effects. We also apply dNET to publicly available TCGA cancer datasets and identify pathways where copy number mediated gene-gene interactions differ between samples with cancer stage lower than stage 3 and samples with cancer stage 3 or above.

A global × global test for testing associations between two large sets of variables.

In high-dimensional omics studies where multiple molecular profiles are obtained for each set of patients, there is often interest in identifying complex multivariate associations, for example, copy number regulated expression levels in a certain pathway or in a genomic region. To detect such associations, we present a novel approach to test for association between two sets of variables. Our approach generalizes the global test, which tests for association between a group of covariates and a single univariate response, to allow high-dimensional multivariate response. We apply the method to several simulated datasets as well as two publicly available datasets, where we compare the performance of multivariate global test (G2) with univariate global test. The method is implemented in R and will be available as a part of the globaltest package in R.

The Characteristics of Heterozygous Protein Truncating Variants in the Human Genome.

Sequencing projects have identified large numbers of rare stop-gain and frameshift variants in the human genome. As most of these are observed in the heterozygous state, they test a gene's tolerance to haploinsufficiency and dominant loss of function. We analyzed the distribution of truncating variants across 16,260 autosomal protein coding genes in 11,546 individuals. We observed 39,893 truncating variants affecting 12,062 genes, which significantly differed from an expectation of 12,916 genes under a model of neutral de novo mutation (p<10-4). Extrapolating this to increasing numbers of sequenced individuals, we estimate that 10.8% of human genes do not tolerate heterozygous truncating variants. An additional 10 to 15% of truncated genes may be rescued by incomplete penetrance or compensatory mutations, or because the truncating variants are of limited functional impact. The study of protein truncating variants delineates the essential genome and, more generally, identifies rare heterozygous variants as an unexplored source of diversity of phenotypic traits and diseases.

A test for comparing two groups of samples when analyzing multiple omics profiles.

BACKGROUND: A number of statistical models has been proposed for studying the association between gene expression and copy number data in integrated analysis. The next step is to compare association patterns between different groups of samples. RESULTS: We propose a method, named dSIM, to find differences in association between copy number and gene expression, when comparing two groups of samples. Firstly, we use ridge regression to correct for the baseline associations between copy number and gene expression. Secondly, the global test is applied to the corrected data in order to find differences in association patterns between two groups of samples. We show that dSIM detects differences even in small genomic regions in a simulation study. We also apply dSIM to two publicly available breast cancer datasets and identify chromosome arms where copy number led gene expression regulation differs between positive and negative estrogen receptor samples. In spite of differing genomic coverage, some selected arms are identified in both datasets. CONCLUSION: We developed a flexible and robust method for studying association differences between two groups of samples while integrating genomic data from different platforms. dSIM can be used with most types of microarray/sequencing data, including methylation and microRNA expression. The method is implemented in R and will be made part of the BioConductor package SIM.

Fused lasso algorithm for Cox' proportional hazards and binomial logit models with application to copy number profiles.

This paper presents an efficient algorithm based on the combination of Newton Raphson and Gradient Ascent, for using the fused lasso regression method to construct a genome-based classifier. The characteristic structure of copy number data suggests that feature selection should take genomic location into account for producing more interpretable results for genome-based classifiers. The fused lasso penalty, an extension of the lasso penalty, encourages sparsity of the coefficients and their differences by penalizing the L1-norm for both of them at the same time, thus using genomic location. The major advantage of the algorithm over other existing fused lasso optimization techniques is its ability to predict binomial as well as survival response efficiently. We apply our algorithm to two publicly available datasets in order to predict survival and binary outcomes.

Viral genome wide association study identifies novel hepatitis C virus polymorphisms associated with sofosbuvir treatment failure.

Persistent hepatitis C virus (HCV) infection is a major cause of chronic liver disease, worldwide. With the development of direct-acting antivirals, treatment of chronically infected patients has become highly effective, although a subset of patients responds less well to therapy. Sofosbuvir is a common component of current de novo or salvage combination therapies, that targets the HCV NS5B polymerase. We use pre-treatment whole-genome sequences of HCV from 507 patients infected with HCV subtype 3a and treated with sofosbuvir containing regimens to detect viral polymorphisms associated with response to treatment. We find three common polymorphisms in non-targeted HCV NS2 and NS3 proteins are associated with reduced treatment response. These polymorphisms are enriched in post-treatment HCV sequences of patients unresponsive to treatment. They are also associated with lower reductions in viral load in the first week of therapy. Using in vitro short-term dose-response assays, these polymorphisms do not cause any reduction in sofosbuvir potency, suggesting an indirect mechanism of action in decreasing sofosbuvir efficacy. The identification of polymorphisms in NS2 and NS3 proteins associated with poor treatment outcomes emphasises the value of systematic genome-wide analyses of viruses in uncovering clinically relevant polymorphisms that impact treatment.

Adaptation of hepatitis C virus to interferon lambda polymorphism across multiple viral genotypes.

Genetic polymorphism in the interferon lambda (IFN-λ) region is associated with spontaneous clearance of hepatitis C virus (HCV) infection and response to interferon-based treatment. Here, we evaluate associations between IFN-λ polymorphism and HCV variation in 8729 patients (Europeans 77%, Asians 13%, Africans 8%) infected with various viral genotypes, predominantly 1a (41%), 1b (22%) and 3a (21%). We searched for associations between rs12979860 genotype and variants in the NS3, NS4A, NS5A and NS5B HCV proteins. We report multiple associations in all tested proteins, including in the interferon-sensitivity determining region of NS5A. We also assessed the combined impact of human and HCV variation on pretreatment viral load and report amino acids associated with both IFN-λ polymorphism and HCV load across multiple viral genotypes. By demonstrating that IFN-λ variation leaves a large footprint on the viral proteome, we provide evidence of pervasive viral adaptation to innate immune pressure during chronic HCV infection.

Correcting for Population Stratification Reduces False Positive and False Negative Results in Joint Analyses of Host and Pathogen Genomes.

Studies of host genetic determinants of pathogen sequence variations can identify sites of genomic conflicts, by highlighting variants that are implicated in immune response on the host side and adaptive escape on the pathogen side. However, systematic genetic differences in host and pathogen populations can lead to inflated type I (false positive) and type II (false negative) error rates in genome-wide association analyses. Here, we demonstrate through a simulation that correcting for both host and pathogen stratification reduces spurious signals and increases power to detect real associations in a variety of tested scenarios. We confirm the validity of the simulations by showing comparable results in an analysis of paired human and HIV genomes.

Genetics of human susceptibility to active and latent tuberculosis: present knowledge and future perspectives.

Tuberculosis is an ancient human disease, estimated to have originated and evolved over thousands of years alongside modern human populations. Despite considerable advances in disease control, tuberculosis remains one of the world's deadliest communicable diseases with 10 million incident cases and 1·8 million deaths in 2015 alone based on the annual WHO report, due to inadequate health service resources in less-developed regions of the world, and exacerbated by the HIV/AIDS pandemic and emergence of multidrug-resistant strains of Mycobacterium tuberculosis. Recent findings from studies of tuberculosis infection and of patients with Mendelian predisposition to severe tuberculosis have started to reveal human loci influencing tuberculosis outcomes. In this Review, we assess the current understanding of the contribution of host genetics to disease susceptibility and to drug treatment. Despite remarkable progress in technology, only a few associated genetic variants have so far been identified, strongly indicating the need for larger global studies that investigate both common and under-represented rare variants to develop new approaches to combat the disease. Pharmacogenomic discoveries are also likely to lead to more efficient drug design and development, and ultimately safer and more effective therapies for tuberculosis.

Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines.

BACKGROUND: Humoral immune responses to infectious agents or vaccination vary substantially among individuals, and many of the factors responsible for this variability remain to be defined. Current evidence suggests that human genetic variation influences (i) serum immunoglobulin levels, (ii) seroconversion rates, and (iii) intensity of antigen-specific immune responses. Here, we evaluated the impact of intrinsic (age and sex), environmental, and genetic factors on the variability of humoral response to common pathogens and vaccines. METHODS: We characterized the serological response to 15 antigens from common human pathogens or vaccines, in an age- and sex-stratified cohort of 1000 healthy individuals (Milieu Intérieur cohort). Using clinical-grade serological assays, we measured total IgA, IgE, IgG, and IgM levels, as well as qualitative (serostatus) and quantitative IgG responses to cytomegalovirus, Epstein-Barr virus, herpes simplex virus 1 and 2, varicella zoster virus, Helicobacter pylori, Toxoplasma gondii, influenza A virus, measles, mumps, rubella, and hepatitis B virus. Following genome-wide genotyping of single nucleotide polymorphisms and imputation, we examined associations between ~ 5 million genetic variants and antibody responses using single marker and gene burden tests. RESULTS: We identified age and sex as important determinants of humoral immunity, with older individuals and women having higher rates of seropositivity for most antigens. Genome-wide association studies revealed significant associations between variants in the human leukocyte antigen (HLA) class II region on chromosome 6 and anti-EBV and anti-rubella IgG levels. We used HLA imputation to fine map these associations to amino acid variants in the peptide-binding groove of HLA-DRß1 and HLA-DPß1, respectively. We also observed significant associations for total IgA levels with two loci on chromosome 2 and with specific KIR-HLA combinations. CONCLUSIONS: Using extensive serological testing and genome-wide association analyses in a well-characterized cohort of healthy individuals, we demonstrated that age, sex, and specific human genetic variants contribute to inter-individual variability in humoral immunity. By highlighting genes and pathways implicated in the normal antibody response to frequently encountered antigens, these findings provide a basis to better understand disease pathogenesis. TRIALS REGISTRATION: ClinicalTrials.gov , NCT01699893.

Genome-to-genome analysis highlights the effect of the human innate and adaptive immune systems on the hepatitis C virus.

Outcomes of hepatitis C virus (HCV) infection and treatment depend on viral and host genetic factors. Here we use human genome-wide genotyping arrays and new whole-genome HCV viral sequencing technologies to perform a systematic genome-to-genome study of 542 individuals who were chronically infected with HCV, predominantly genotype 3. We show that both alleles of genes encoding human leukocyte antigen molecules and genes encoding components of the interferon lambda innate immune system drive viral polymorphism. Additionally, we show that IFNL4 genotypes determine HCV viral load through a mechanism dependent on a specific amino acid residue in the HCV NS5A protein. These findings highlight the interplay between the innate immune system and the viral genome in HCV control.

Grey Matter Atrophy in Multiple Sclerosis: Clinical Interpretation Depends on Choice of Analysis Method.

BACKGROUND: Studies disagree on the location of grey matter (GM) atrophy in the multiple sclerosis (MS) brain. AIM: To examine the consistency between FSL, FreeSurfer, SPM for GM atrophy measurement (for volumes, patient/control discrimination, and correlations with cognition). MATERIALS AND METHODS: 127 MS patients and 50 controls were included and cortical and deep grey matter (DGM) volumetrics were performed. Consistency of volumes was assessed with Intraclass Correlation Coefficient/ICC. Consistency of patients/controls discrimination was assessed with Cohen's d, t-tests, MANOVA and a penalized double-loop logistic classifier. Consistency of association with cognition was assessed with Pearson correlation coefficient and ANOVA. Voxel-based morphometry (SPM-VBM and FSL-VBM) and vertex-wise FreeSurfer were used for group-level comparisons. RESULTS: The highest volumetry ICC were between SPM and FreeSurfer for cortical regions, and the lowest between SPM and FreeSurfer for DGM. The caudate nucleus and temporal lobes had high consistency between all software, while amygdala had lowest volumetric consistency. Consistency of patients/controls discrimination was largest in the DGM for all software, especially for thalamus and pallidum. The penalized double-loop logistic classifier most often selected the thalamus, pallidum and amygdala for all software. FSL yielded the largest number of significant correlations. DGM yielded stronger correlations with cognition than cortical volumes. Bilateral putamen and left insula volumes correlated with cognition using all methods. CONCLUSION: GM volumes from FreeSurfer, FSL and SPM are different, especially for cortical regions. While group-level separation between MS and controls is comparable, correlations between regional GM volumes and clinical/cognitive variables in MS should be cautiously interpreted.