BigTop: A Three-Dimensional Virtual Reality tool for GWAS Visualization

BackgroundGenome-wide association studies (GWAS) are typically visualized using a two-dimensional Manhattan plot, displaying chromosomal location of SNPs along the x-axis and the negative log-10 of their p-value on the y-axis. This traditional plot provides a broad overview of the results, but offers little opportunity for interaction or expansion of specific regions, and is unable to show additional dimensions of the dataset.ResultsWe created BigTop, a visualization framework in virtual reality (VR), designed to render a Manhattan plot in three dimensions, wrapping the graph around the user in a simulated cylindrical room. BigTop uses the z-axis to display minor allele frequency of each SNP, allowing for the identification of allelic variants of genes. BigTop also offers additional interactivity, allowing users to select any individual SNP and receive expanded information, including SNP name, exact values, and gene location, if applicable. BigTop is built in JavaScript using the React and A-Frame frameworks, and can be rendered using commercially available VR headsets or in a two-dimensional web browser such as Google Chrome. Data is read into BigTop in JSON format, and can be provided as either JSON or a tab-separated text file.ConclusionsUsing additional dimensions and interactivity options offered through VR, we provide a new, interactive, three-dimensional representation of the traditional Manhattan plot for displaying and exploring GWAS data.

Is it reasonable to account for population structure in genome-wide association studies?

Population structure is widely perceived as a noise factor that undermines the quality of association between an SNP variable and a phenotypic variable in genome-wide association studies (GWAS). The linear model for GWAS generally accounts for population-structure variables to obtain the adjusted phenotype which has less noise. Its result is known to amplify the contrast between significant SNPs and insignificant SNPs in a resultant Manhattan plot. In fact, however, conventional GWAS practice often implements the linear model in an unusual way in that the population-structure variables are incorporated into the linear model in the form of continuous variables rather than factor variables. If the coefficients for population-structure variables change across all SNPs, then each SNP variable will be regressed against a differently adjusted phenotypic variable, making the GWAS process unreliable. Focusing on this concern, this study investigated whether accounting for population-structure variables in the linear model for GWAS can assure the adjusted phenotypes to be consistent across all SNPs. The result showed that the adjusted phenotypes resulting across all SNPs were not consistent, which is alarming considering conventional GWAS practice that accounts for population structure.

Alignment-Free Approaches for Predicting Novel Nuclear Mitochondrial Segments (NUMTs) in the Human Genome

The nuclear human genome harbors sequences of mitochondrial origin, indicating an ancestral transfer of DNA from the mitogenome. Several Nuclear Mitochondrial Segments (NUMTs) have been detected by alignment-based sequence similarity search, as implemented in the Basic Local Alignment Search Tool (BLAST). Identifying NUMTs is important for the comprehensive annotation and understanding of the human genome. Here we explore the possibility of detecting NUMTs in the human genome by alignment-free sequence similarity search, such as k-mers (k-tuples, k-grams, oligos of length k) distributions. We find that when k=6 or larger, the k-mer approach and BLAST search produce almost identical results, e.g., detect the same set of NUMTs longer than 3kb. However, when k=5 or k=4, certain signals are only detected by the alignment-free approach, and these may indicate yet unrecognized, and potentially more ancestral NUMTs. We introduce a “Manhattan plot” style representation of NUMT predictions across the genome, which are calculated based on the reciprocal of the Jensen-Shannon divergence between the nuclear and mitochondrial k-mer frequencies. The further inspection of the k-mer-based NUMT predictions however shows that most of them contain long-terminal-repeat (LTR) annotations, whereas BLAST-based NUMT predictions do not. Thus, similarity of the mitogenome to LTR sequences is recognized, which we validate by finding the mitochondrial k-mer distribution closer to those for transposable sequences and specifically, close to some types of LTR.

High-resolution DNA accessibility profiles increase the discovery and interpretability of genetic associations

Genetic risk for common autoimmune diseases is influenced by hundreds of small effect, mostly non-coding variants, enriched in regulatory regions active in adaptive-immune cell types. DNaseI hypersensitivity sites (DHSs) are a genomic mark for regulatory DNA. Here, we generated a single DHSs annotation from fifteen deeply sequenced DNase-seq experiments in adaptive-immune as well as non-immune cell types. Using this annotation we quantified accessibility across cell types in a matrix format amenable to statistical analysis, deduced the subset of DHSs unique to adaptive-immune cell types, and grouped DHSs by cell-type accessibility profiles. Measuring enrichment with cell-type-specific TF binding sites as well as proximal gene expression and function, we show that accessibility profiles grouped DHSs into coherent regulatory functions. Using the adaptive-immune-specific DHSs as input (0.37% of genome), we associated DHSs to six autoimmune diseases with GWAS data. Associated loci showed higher replication rates when compared to loci identified by GWAS or by considering all DHSs, allowing the additional discovery of 327 loci (FDR<0.005) below typical GWAS significance threshold, 52 of which are novel and replicating discoveries. Finally, we integrated DHS associations from six autoimmune diseases, using a network model (bird’-eye view) and a regulatory Manhattan plot schema (per locus). Taken together, we described and validated a strategy to leverage finely resolved regulatory priors, enhancing the discovery, interpretability, and resolution of genetic associations, and providing actionable insights for follow up work.

Genetic Risk Factors for the Development of Osteonecrosis in Children Under Age 10 Treated for Acute Lymphoblastic Leukemia

Background: Therapy induced osteonecrosis has become a limiting toxicity in the intensification of treatment for pediatric acute lymphoblastic leukemia (ALL), particularly among patients 10 to 20 years of age. Prior studies on the genetic determinants of osteonecrosis have focused primarily on patients older than 10 years, leaving the genetic risk factors for the larger group of children with ALL less than 10 years old incompletely understood. It is hypothesized that genetic risk factors may account for a greater proportion of risk of osteonecrosis or involve differing mechanisms in younger than in older patients. Methods: We performed the first evaluation of genetic risk factors for osteonecrosis in children less than 10 years old using a discovery cohort of 82 cases of osteonecrosis and 287 controls treated on Children's Oncology Group (COG) protocol AALL0331 (NCI standard risk ALL) and tested for replication in 817 children less than 10 treated on COG protocol AALL0232 (high risk ALL). Genotyping was performed using the Affymetrix Gene Chip Human Mapping Array 6.0 and the Illumina Human Exome BeadChip v1.1. A subset of 15 cases in the discovery cohort had coding variant calls verified by whole exome sequencing. Both discovery and replication genome-wide association studies (GWAS) adjusted for demographic and therapy variables known to modify the risk of osteonecrosis. Enhancer enrichment analysis using HaploReg identified tissues affected by the identified single nucleotide polymorphisms (SNPs). Genes associated with the identified SNPs were evaluated using Ingenuity Pathway Analysis for enrichment in biologically relevant pathways. Results: Within the discovery cohort, top ranked variants were rs76599360 and rs77556622 which were in full linkage disequilibrium [P=1.13x10-9, odds ratio (OR) 22.0, 95% confidence interval (95%CI) 8.15-59.6] located near bone morphogenic protein 7 (BMP7). The top replicated SNPs were located near BMP7 [rs75161997, P=5.34x10-8 (OR 15.0; 95%CI 5.64-39.7) and P =0.0498 (OR 8.44; 95%CI 1.002-71.1) in the discovery and replication cohorts, respectively] and PROX1-antisense RNA1 [PROX1-AS1:rs1891059, P=2.28x10-7 (OR 6.48; 95%CI 3.19-13.1) and P=0.0077 (OR 3.78; 95%CI 1.42-10.1) for the discovery and replication cohorts, respectively]. The top replicated non-synonymous SNP, rs34144324, was in a glutamate receptor gene [GRID2, P=8.65x10-6 (OR 3.46; 95%CI 2.00-5.98) and 0.0136 (OR 10.8; 95%CI 1.63-71.4) in the discovery and replication cohorts, respectively], and the genotyping of this variant was verified in the whole exome sequencing data. In a meta-analysis of both cohorts, the replicated BMP7 and PROX1-AS1 variants (rs75161997 and rs1891059, respectively) and a variant in NCRNA00251 (rs141059755) met the genome-wide significance threshold of <5x10-8 (Figure 1). In a meta-analysis of both cohorts, replicated SNPs with meta-analysis P<1 x10-5 showed enrichment in enhancers active in mesenchymal stem cells. Pathway analysis of genes linked to top SNPs (meta-analysis P <0.001) demonstrated enrichment in glutamate receptor signaling and adipogenesis pathways. Conclusions: Variants in genes important to bone and fat differentiation from mesenchymal stem cells were associated with osteonecrosis in children less than 10 years old. The importance of variants in glutamate receptor signaling in children less than 10 also confirms the findings of a recently completed GWAS of osteonecrosis in AALL0232 (Blood 2014 124:367; 2014) including patients of all ages and in which osteonecrosis occurred primarily in older children. These data provide new insights into osteonecrosis with implications for patients of all ages. Figure 1. Manhattan plot of meta-analysis for osteonecrosis risk in children <10 years old Figure 1. Manhattan plot of meta-analysis for osteonecrosis risk in children <10 years old Disclosures Hunger: Sigma Tau: Consultancy; Jazz Pharmaceuticals: Consultancy; Merck: Equity Ownership; Spectrum Pharmaceuticals: Consultancy.