Analysis of five deep-sequenced trio-genomes of the Peninsular Malaysia Orang Asli and North Borneo populations.

BACKGROUND: Recent advances in genomic technologies have facilitated genome-wide investigation of human genetic variations. However, most efforts have focused on the major populations, yet trio genomes of indigenous populations from Southeast Asia have been under-investigated. RESULTS: We analyzed the whole-genome deep sequencing data (~ 30×) of five native trios from Peninsular Malaysia and North Borneo, and characterized the genomic variants, including single nucleotide variants (SNVs), small insertions and deletions (indels) and copy number variants (CNVs). We discovered approximately 6.9 million SNVs, 1.2 million indels, and 9000 CNVs in the 15 samples, of which 2.7% SNVs, 2.3% indels and 22% CNVs were novel, implying the insufficient coverage of population diversity in existing databases. We identified a higher proportion of novel variants in the Orang Asli (OA) samples, i.e., the indigenous people from Peninsular Malaysia, than that of the North Bornean (NB) samples, likely due to more complex demographic history and long-time isolation of the OA groups. We used the pedigree information to identify de novo variants and estimated the autosomal mutation rates to be 0.81 × 10- 8 - 1.33 × 10- 8, 1.0 × 10- 9 - 2.9 × 10- 9, and ~ 0.001 per site per generation for SNVs, indels, and CNVs, respectively. The trio-genomes also allowed for haplotype phasing with high accuracy, which serves as references to the future genomic studies of OA and NB populations. In addition, high-frequency inherited CNVs specific to OA or NB were identified. One example is a 50-kb duplication in DEFA1B detected only in the Negrito trios, implying plausible effects on host defense against the exposure of diverse microbial in tropical rainforest environment of these hunter-gatherers. The CNVs shared between OA and NB groups were much fewer than those specific to each group. Nevertheless, we identified a 142-kb duplication in AMY1A in all the 15 samples, and this gene is associated with the high-starch diet. Moreover, novel insertions shared with archaic hominids were identified in our samples. CONCLUSION: Our study presents a full catalogue of the genome variants of the native Malaysian populations, which is a complement of the genome diversity in Southeast Asians. It implies specific population history of the native inhabitants, and demonstrated the necessity of more genome sequencing efforts on the multi-ethnic native groups of Malaysia and Southeast Asia.

Genomic structure of the native inhabitants of Peninsular Malaysia and North Borneo suggests complex human population history in Southeast Asia.

Southeast Asia (SEA) is enriched with a complex history of peopling. Malaysia, which is located at the crossroads of SEA, has been recognized as one of the hubs for early human migration. To unravel the genomic complexity of the native inhabitants of Malaysia, we sequenced 12 samples from 3 indigenous populations from Peninsular Malaysia and 4 native populations from North Borneo to a high coverage of 28-37×. We showed that the Negritos from Peninsular Malaysia shared a common ancestor with the East Asians, but exhibited some level of gene flow from South Asia, while the North Borneo populations exhibited closer genetic affinity towards East Asians than the Malays. The analysis of time of divergence suggested that ancestors of Negrito were the earliest settlers in the Malay Peninsula, whom first separated from the Papuans ~ 50-33 thousand years ago (kya), followed by East Asian (~ 40-15 kya), while the divergence time frame between North Borneo and East Asia populations predates the Austronesian expansion period implies a possible pre-Neolithic colonization. Substantial Neanderthal ancestry was confirmed in our genomes, as was observed in other East Asians. However, no significant difference was observed, in terms of the proportion of Denisovan gene flow into these native inhabitants from Malaysia. Judging from the similar amount of introgression in the Southeast Asians and East Asians, our findings suggest that the Denisovan gene flow may have occurred before the divergence of these populations and that the shared similarities are likely an ancestral component.

Insights into the genetic structure and diversity of 38 South Asian Indians from deep whole-genome sequencing.

South Asia possesses a significant amount of genetic diversity due to considerable intergroup differences in culture and language. There have been numerous reports on the genetic structure of Asian Indians, although these have mostly relied on genotyping microarrays or targeted sequencing of the mitochondria and Y chromosomes. Asian Indians in Singapore are primarily descendants of immigrants from Dravidian-language-speaking states in south India, and 38 individuals from the general population underwent deep whole-genome sequencing with a target coverage of 30X as part of the Singapore Sequencing Indian Project (SSIP). The genetic structure and diversity of these samples were compared against samples from the Singapore Sequencing Malay Project and populations in Phase 1 of the 1,000 Genomes Project (1 KGP). SSIP samples exhibited greater intra-population genetic diversity and possessed higher heterozygous-to-homozygous genotype ratio than other Asian populations. When compared against a panel of well-defined Asian Indians, the genetic makeup of the SSIP samples was closely related to South Indians. However, even though the SSIP samples clustered distinctly from the Europeans in the global population structure analysis with autosomal SNPs, eight samples were assigned to mitochondrial haplogroups that were predominantly present in Europeans and possessed higher European admixture than the remaining samples. An analysis of the relative relatedness between SSIP with two archaic hominins (Denisovan, Neanderthal) identified higher ancient admixture in East Asian populations than in SSIP. The data resource for these samples is publicly available and is expected to serve as a valuable complement to the South Asian samples in Phase 3 of 1 KGP.

Deep whole-genome sequencing of 100 southeast Asian Malays.

Whole-genome sequencing across multiple samples in a population provides an unprecedented opportunity for comprehensively characterizing the polymorphic variants in the population. Although the 1000 Genomes Project (1KGP) has offered brief insights into the value of population-level sequencing, the low coverage has compromised the ability to confidently detect rare and low-frequency variants. In addition, the composition of populations in the 1KGP is not complete, despite the fact that the study design has been extended to more than 2,500 samples from more than 20 population groups. The Malays are one of the Austronesian groups predominantly present in Southeast Asia and Oceania, and the Singapore Sequencing Malay Project (SSMP) aims to perform deep whole-genome sequencing of 100 healthy Malays. By sequencing at a minimum of 30× coverage, we have illustrated the higher sensitivity at detecting low-frequency and rare variants and the ability to investigate the presence of hotspots of functional mutations. Compared to the low-pass sequencing in the 1KGP, the deeper coverage allows more functional variants to be identified for each person. A comparison of the fidelity of genotype imputation of Malays indicated that a population-specific reference panel, such as the SSMP, outperforms a cosmopolitan panel with larger number of individuals for common SNPs. For lower-frequency (<5%) markers, a larger number of individuals might have to be whole-genome sequenced so that the accuracy currently afforded by the 1KGP can be achieved. The SSMP data are expected to be the benchmark for evaluating the value of deep population-level sequencing versus low-pass sequencing, especially in populations that are poorly represented in population-genetics studies.

iCall: a genotype-calling algorithm for rare, low-frequency and common variants on the Illumina exome array.

MOTIVATION: Next-generation genotyping microarrays have been designed with insights from 1000 Genomes Project and whole-exome sequencing studies. These arrays additionally include variants that are typically present at lower frequencies. Determining the genotypes of these variants from hybridization intensities is challenging because there is less support to locate the presence of the minor alleles when the allele counts are low. Existing algorithms are mainly designed for calling common variants and are notorious for failing to generate accurate calls for low-frequency and rare variants. Here, we introduce a new calling algorithm, iCall, to call genotypes for variants across the whole spectrum of allele frequencies. RESULTS: We benchmarked iCall against four of the most commonly used algorithms, GenCall, optiCall, illuminus and GenoSNP, as well as a post-processing caller zCall that adopted a two-stage calling design. Normalized hybridization intensities for 12 370 individuals genotyped on the Illumina HumanExome BeadChip were considered, of which 81 individuals were also whole-genome sequenced. The sequence calls were used to benchmark the accuracy of the genotype calling, and our comparisons indicated that iCall outperforms all four single-stage calling algorithms in terms of call rates and concordance, particularly in the calling accuracy of minor alleles, which is the principal concern for rare and low-frequency variants. The application of zCall to post-process the output from iCall also produced marginally improved performance to the combination of zCall and GenCall. AVAILABILITY AND IMPLEMENTATION: iCall is implemented in C++ for use on Linux operating systems and is available for download at http://www.statgen.nus.edu.sg/∼software/icall.html.

Viral quasispecies inference from 454 pyrosequencing.

BACKGROUND: Many potentially life-threatening infectious viruses are highly mutable in nature. Characterizing the fittest variants within a quasispecies from infected patients is expected to allow unprecedented opportunities to investigate the relationship between quasispecies diversity and disease epidemiology. The advent of next-generation sequencing technologies has allowed the study of virus diversity with high-throughput sequencing, although these methods come with higher rates of errors which can artificially increase diversity. RESULTS: Here we introduce a novel computational approach that incorporates base quality scores from next-generation sequencers for reconstructing viral genome sequences that simultaneously infers the number of variants within a quasispecies that are present. Comparisons on simulated and clinical data on dengue virus suggest that the novel approach provides a more accurate inference of the underlying number of variants within the quasispecies, which is vital for clinical efforts in mapping the within-host viral diversity. Sequence alignments generated by our approach are also found to exhibit lower rates of error. CONCLUSIONS: The ability to infer the viral quasispecies colony that is present within a human host provides the potential for a more accurate classification of the viral phenotype. Understanding the genomics of viruses will be relevant not just to studying how to control or even eradicate these viral infectious diseases, but also in learning about the innate protection in the human host against the viruses.

Singapore Pharmacogenomics Portal: a web resource for evaluating human genetic variations of genes responsible for drug responses.

The Singapore Pharmacogenomics Portal is the first genomics web platform that links public resources from PharmGKB and DrugBank with population genetics data from the International HapMap Project and the Singapore Genome Variation Project. The web portal provides the opportunity to survey genetic differences across populations for all autosomal genes in the genome, and serves as an integrated platform for linking these data with drugs and genetic variants that affect drug responses, adverse reactions, and dosage requirements. We envisage that the information provided by the portal will be useful to drug regulators and clinical researchers when evaluating the transferability of results from clinical trials conducted in one population to other populations for which no direct clinical testing has been conducted. The utility of this resource may extend to other countries in the region that also have significant populations of Chinese, Malay, or Indian ancestry.

Stressed target cancer cells drive nongenetic reprogramming of CAR T cells and tumor microenvironment, overcoming multiple obstacles of CAR T therapy for solid tumors.

The poor efficacy of chimeric antigen receptor T-cell therapy (CAR T) for solid tumor is due to insufficient CAR T cell tumor infiltration, in vivo expansion, persistence, and effector function, as well as exhaustion, intrinsic target antigen heterogeneity or antigen loss of target cancer cells, and immunosuppressive tumor microenvironment (TME). Here we describe a broadly applicable nongenetic approach that simultaneously addresses the multiple challenges of CAR T as a therapy for solid tumors. The approach massively reprograms CAR T cells by exposing them to stressed target cancer cells which have been exposed to the cell stress inducer disulfiram (DSF) and copper (Cu)(DSF/Cu) plus ionizing irradiation (IR). The reprogrammed CAR T cells acquired early memory-like characteristics, potent cytotoxicity, enhanced in vivo expansion, persistence, and decreased exhaustion. Tumors stressed by DSF/Cu and IR also reprogrammed and reversed immunosuppressive TME in humanized mice. The reprogrammed CAR T cells, derived from peripheral blood mononuclear cells (PBMC) of healthy or metastatic breast cancer patients, induced robust, sustained memory and curative anti-solid tumor responses in multiple xenograft mouse models, establishing proof of concept for empowering CAR T by stressing tumor as a novel therapy for solid tumor.

Stressed target cancer cells drive nongenetic reprogramming of CAR T cells and solid tumor microenvironment.

The poor efficacy of chimeric antigen receptor T-cell therapy (CAR T) for solid tumors is due to insufficient CAR T cell tumor infiltration, in vivo expansion, persistence, and effector function, as well as exhaustion, intrinsic target antigen heterogeneity or antigen loss of target cancer cells, and immunosuppressive tumor microenvironment (TME). Here we describe a broadly applicable nongenetic approach that simultaneously addresses the multiple challenges of CAR T as a therapy for solid tumors. The approach reprograms CAR T cells by exposing them to stressed target cancer cells which have been exposed to the cell stress inducer disulfiram (DSF) and copper (Cu)(DSF/Cu) plus ionizing irradiation (IR). The reprogrammed CAR T cells acquire early memory-like characteristics, potent cytotoxicity, enhanced in vivo expansion, persistence, and decreased exhaustion. Tumors stressed by DSF/Cu and IR also reprogram and reverse the immunosuppressive TME in humanized mice. The reprogrammed CAR T cells, derived from peripheral blood mononuclear cells of healthy donors or metastatic female breast cancer patients, induce robust, sustained memory and curative anti-solid tumor responses in multiple xenograft mouse models, establishing proof of concept for empowering CAR T by stressing tumor as a promising therapy for solid tumors.

Sleep exerts lasting effects on hematopoietic stem cell function and diversity.

A sleepless night may feel awful in its aftermath, but sleep's revitalizing powers are substantial, perpetuating the idea that convalescent sleep is a consequence-free physiological reset. Although recent studies have shown that catch-up sleep insufficiently neutralizes the negative effects of sleep debt, the mechanisms that control prolonged effects of sleep disruption are not understood. Here, we show that sleep interruption restructures the epigenome of hematopoietic stem and progenitor cells (HSPCs) and increases their proliferation, thus reducing hematopoietic clonal diversity through accelerated genetic drift. Sleep fragmentation exerts a lasting influence on the HSPC epigenome, skewing commitment toward a myeloid fate and priming cells for exaggerated inflammatory bursts. Combining hematopoietic clonal tracking with mathematical modeling, we infer that sleep preserves clonal diversity by limiting neutral drift. In humans, sleep restriction alters the HSPC epigenome and activates hematopoiesis. These findings show that sleep slows decay of the hematopoietic system by calibrating the hematopoietic epigenome, constraining inflammatory output, and maintaining clonal diversity.

RNA sequencing data from neutrophils of patients with cystic fibrosis reveals potential for developing biomarkers for pulmonary exacerbations.

BACKGROUND: There is no effective way to predict cystic fibrosis (CF) pulmonary exacerbations (CFPE) before they become symptomatic or to assess satisfactory treatment responses. METHODS: RNA sequencing of peripheral blood neutrophils from CF patients before and after therapy for CFPE was used to create transcriptome profiles. Transcripts with an average transcripts per million (TPM) level > 1.0 and a false discovery rate (FDR) < 0.05 were used in a cosine K-nearest neighbor (KNN) model. Real time PCR was used to corroborate RNA sequencing expression differences in both neutrophils and whole blood samples from an independent cohort of CF patients. Furthermore, sandwich ELISA was conducted to assess plasma levels of MRP8/14 complexes in CF patients before and after therapy. RESULTS: We found differential expression of 136 transcripts and 83 isoforms when we compared neutrophils from CF patients before and after therapy (>1.5 fold change, FDR-adjusted P < 0.05). The model was able to successfully separate CF flare samples from those taken from the same patients in convalescence with an accuracy of 0.75 in both the training and testing cohorts. Six differently expressed genes were confirmed by real time PCR using both isolated neutrophils and whole blood from an independent cohort of CF patients before and after therapy, even though levels of myeloid related protein MRP8/14 dimers in plasma of CF patients were essentially unchanged by therapy. CONCLUSIONS: Our findings demonstrate the potential of machine learning approaches for classifying disease states and thus developing sensitive biomarkers that can be used to monitor pulmonary disease activity in CF.

Efficient identification of neoantigen-specific T-cell responses in advanced human ovarian cancer.

BACKGROUND: Efficient identification of neoantigen-specific T-cell responses in epithelial ovarian cancer (EOC) remains a challenge. Existing investigations of spontaneous T-cell response to tumor neoepitope in EOC have taken the approach of comprehensive screening all neoantigen candidates, with a validation rate of 0.5-2%. METHODS: Whole-exome and transcriptome sequencing analysis of treatment-naive EOC patients were performed to identify neoantigen candidates, and the immunogenicity of prioritized neoantigens was evaluated by analyzing spontaneous neoantigen-specfic CD4+ and CD8+ T-cell responses in the tumor and/or peripheral blood. The biological relevance of neoantigen-specific T-cell lines and clones were analyzed by evaluating the capacity of autologous ovarian tumor recognition. Genetic transfer of T-cell receptor (TCR) from these neoantigen-specific T-cell clones into peripheral blood T-cells was conducted to generate neoepitope-specific T-cells. The molecular signature associated with positive neoantigen T-cell responses was investigated, and the impacts of expression level and lymphocyte source on neoantigen identification were explored. RESULTS: Using a small subset of prioritized neoantigen candidates, we were able to detect spontaneous CD4+ and/or CD8+ T-cell responses against neoepitopes from autologous lymphocytes in half of treatment-naïve EOC patients, with a significantly improved validation rate of 19%. Tumors from patients exhibiting neoantigen-specific T-cell responses exhibited a signature of upregulated antigen processing and presentation machinery, which was also associated with favorable patient survival in the TCGA ovarian cohort. T-cells specific against two mutated cancer-associated genes, NUP214 and JAK1, recognized autologous tumors. Gene-engineering with TCR from these neoantigen-specific T-cell clones conferred neoantigen-reactivity to peripheral T-cells. CONCLUSIONS: Our study demonstrated the feasibility of efficiently identifying both CD4+ and CD8+ neoantigen-specific T-cells in EOC. Autologous lymphocytes genetically engineered with tumor antigen-specific TCR can be used to generate cells for use in the personalized adoptive T-cell transfer immunotherapy.

Soluble inflammatory mediators induce transcriptional re-organization that is independent of dna methylation changes in cultured human chorionic villous trophoblasts.

The studies proposed here were undertaken to test the hypothesis that, under specific circumstances (e.g., a strong enough inflammatory stimulus), genes that are repressed at the maternal-fetal interface via DNA methylation might be de-methylated, allowing either a maternal immune response to the semi-allogenic fetus or the onset of early labor. Chorionic trophoblasts (CT) were isolated from fetal membranes, followed by incubation with medium from LPS-activated PBMC or resting PBMC medium for 2 h. RNA and DNA were isolated from the cells for RNA-seq and DNA methylation studies. Two hrs after being exposed to conditioned medium from LPS-activated PBMC, CT showed differential expression of 114 genes, all but 2 of which showed higher expression in the stimulated cells than is the unstimulated cells. We also identified 318 differentially methylated regions (DMRs) that associated with 306 genes (155 protein coding genes) in the two groups, but the observed methylation changes had negligible impact on the observed transcriptional changes in CT. CT display complex patterns of transcription in response to inflammation. DNA methylation does not appear to be an important regulator of the observed transcriptional changes.

Genetic insights into juvenile idiopathic arthritis derived from deep whole genome sequencing.

Deep whole genome sequencing (WGS) allows for the comprehensive study of genetic landscapes at finer resolution than array based methods. We conducted deep WGS on children with the polyarticular form of juvenile idiopathic arthritis (JIA), using 2 independent cohorts to ascertain the sequencing fidelity. Genome wide SNP density analysis identified 18 SNP hotspots with comparison to the 1000 Genome Projects (1KGP) data. A subset of the genes adjacent to SNP hotspots showed statistically significant enrichment in immunological processes. Genes adjacent to indel hotspots were functionally related to G-protein coupled signaling pathways. Further analyses elucidated significantly more JIA SNPs with regulatory potential compared to 1KGP data. Furthermore, SNPs located within linkage disequibilium (LD) blocks containing previously identified JIA-associated SNPs demonstrated higher regulation potential compared to SNPs outside LD blocks. We also demonstrated enrichment of novel JIA variants in histone modification peaks and DNase hypersensitivity sites in B cells. This study greatly expands the number of genetic variants that may contribute to JIA and give us some clues into what may trigger this disease. To date, this study is the first deep WGS effort on children with JIA and provides useful genetic resources for research communities particularly in understanding JIA etiology.

Chromatin landscapes and genetic risk for juvenile idiopathic arthritis.

BACKGROUND: The transcriptomes of peripheral blood cells in children with juvenile idiopathic arthritis (JIA) have distinct transcriptional aberrations that suggest impairment of transcriptional regulation. To gain a better understanding of this phenomenon, we studied known JIA genetic risk loci, the majority of which are located in non-coding regions, where transcription is regulated and coordinated on a genome-wide basis. We examined human neutrophils and CD4 primary T cells to identify genes and functional elements located within those risk loci. METHODS: We analyzed RNA sequencing (RNA-Seq) data, H3K27ac and H3K4me1 chromatin immunoprecipitation-sequencing (ChIP-Seq) data, and previously published chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data to characterize the chromatin landscapes within the known JIA-associated risk loci. RESULTS: In both neutrophils and primary CD4+ T cells, the majority of the JIA-associated linkage disequilibrium (LD) blocks contained H3K27ac and/or H3K4me1 marks. These LD blocks were also binding sites for a small group of transcription factors, particularly in neutrophils. Furthermore, these regions showed abundant intronic and intergenic transcription in neutrophils. In neutrophils, none of the genes that were differentially expressed between untreated patients with JIA and healthy children were located within the JIA-risk LD blocks. In CD4+ T cells, multiple genes, including HLA-DQA1, HLA-DQB2, TRAF1, and IRF1 were associated with the long-distance interacting regions within the LD regions as determined from ChIA-PET data. CONCLUSIONS: These findings suggest that genetic risk contributes to the aberrant transcriptional control observed in JIA. Furthermore, these findings demonstrate the challenges of identifying the actual causal variants within complex genomic/chromatin landscapes.

Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study.

The Singapore Integrative Omics Study provides valuable insights on establishing population reference measurement in 364 Chinese, Malay, and Indian individuals. These measurements include > 2.5 millions genetic variants, 21,649 transcripts expression, 282 lipid species quantification, and 284 clinical, lifestyle, and dietary variables. This concept paper introduces the depth of the data resource, and investigates the extent of ethnic variation at these omics and non-omics biomarkers. It is evident that there are specific biomarkers in each of these platforms to differentiate between the ethnicities, and intra-population analyses suggest that Chinese and Indians are the most biologically homogeneous and heterogeneous, respectively, of the three groups. Consistent patterns of correlations between lipid species also suggest the possibility of lipid tagging to simplify future lipidomics assays. The Singapore Integrative Omics Study is expected to allow the characterization of intra-omic and inter-omic correlations within and across all three ethnic groups through a systems biology approach.The Singapore Genome Variation projects characterized the genetics of Singapore's Chinese, Malay, and Indian populations. The Singapore Integrative Omics Study introduced here goes further in providing multi-omic measurements in individuals from these populations, including genetic, transcriptome, lipidome, and lifestyle data, and will facilitate the study of common diseases in Asian communities.

Limits of Peripheral Blood Mononuclear Cells for Gene Expression-Based Biomarkers in Juvenile Idiopathic Arthritis.

Juvenile Idiopathic Arthritis (JIA) is one of the most common chronic disease conditions affecting children in the USA. As with many rheumatic diseases, there is growing interest in using genomic technologies to develop biomarkers for either diagnosis or to guide treatment ("personalized medicine"). Here, we explore the use of gene expression patterns in peripheral blood mononuclear cells (PBMC) as a first step approach to developing such biomarkers. Although PBMC carry many theoretical advantages for translational research, we have found that sample heterogeneity makes RNASeq on PBMC unsuitable as a first-step method for screening biomarker candidates in JIA. RNASeq studies of homogeneous cell populations are more likely to be useful and informative.

Whole blood expression profiling from the TREAT trial: insights for the pathogenesis of polyarticular juvenile idiopathic arthritis.

BACKGROUND: The Trial of Early Aggressive Therapy in Juvenile Idiopathic Arthritis (TREAT trial) was accompanied by a once-in-a-generation sample collection for translational research. In this paper, we report the results of whole blood gene expression analyses and genomic data-mining designed to cast light on the immunopathogenesis of polyarticular juvenile idiopathic arthritis (JIA). METHODS: TREAT samples and samples from an independent cohort were analyzed on Affymetrix microarrays and compared to healthy controls. Data from the independent cohort were used to validate the TREAT data. Pathways analysis was used to characterize gene expression profiles. Furthermore, we correlated differential gene expression with new information about functional regulatory elements within the genome to develop models of aberrant gene expression in JIA. RESULTS: There was a strong concordance in gene expression between TREAT samples and the independent cohort. In addition, rheumatoid factor (RF)-positive and RF-negative patients showed only small differences on whole blood expression profiles. Analysis of the combined samples showed 158 genes represented by 176 probes that showed differential expression between TREAT subjects at baseline and healthy controls. None of the differentially expressed genes were encoded within linkage disequilibrium blocks containing single nucleotide polymorphisms known to be associated with risk for JIA. Functional analysis of these genes showed functional associations with multiple processes associated with innate and adaptive immunity, and appeared to reflect overall suppression of STAT1-3/interferon response factor-mediated pathways. CONCLUSIONS: Despite their limitations, whole blood expression profiles clearly distinguish children with polyarticular JIA from healthy controls. Whole blood expression profiles identify several immunologic pathways of biologic relevance that will need to be pursued in homogeneous cell populations in order to clarify mechanisms of pathogenesis. TRIAL REGISTRATION: ClinicalTrials.gov registry #NCT00443430 , originally registered 2 March 2007 and last updated 30 May 2013.

Evaluating the coverage and potential of imputing the exome microarray with next-generation imputation using the 1000 Genomes Project.

Next-generation genotyping microarrays have been designed with insights from large-scale sequencing of exomes and whole genomes. The exome genotyping arrays promise to query the functional regions of the human genome at a fraction of the sequencing cost, thus allowing large number of samples to be genotyped. However, two pertinent questions exist: firstly, how representative is the content of the exome chip for populations not involved in the design of the chip; secondly, can the content of the exome chip be imputed with the reference data from the 1000 Genomes Project (1KGP). By deep whole-genome sequencing two Asian populations that are not part of the 1KGP, comprising 96 Southeast Asian Malays and 36 South Asian Indians for which the same samples have also been genotyped on both the Illumina 2.5 M and exome microarrays, we discovered the exome chip is a poor representation of exonic content in our two populations. However, up to 94.1% of the variants on the exome chip that are polymorphic in our populations can be confidently imputed with existing non-exome-centric microarrays using the 1KGP panel. The coverage further increases if there exists population-specific reference data from whole-genome sequencing. There is thus limited gain in using the exome chip for populations not involved in the microarray design. Instead, for the same cost of genotyping 2,000 samples on the exome chip, performing whole-genome sequencing of at least 35 samples in that population to complement the 1KGP may yield a higher coverage of the exonic content from imputation instead.