An eQTL biological data visualization challenge and approaches from the visualization community

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains

A survey of best practices for RNA-seq data analysis.

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.This is the final published version. It first appeared at http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8

The single-cell eQTLGen consortium

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.</p

Advanced Visual Analytics Approaches for the Integrative Study of Genomic and Transcriptomic Data

The advances in next-generation sequencing (NGS) technology enabled rapid and cost-effective whole genome analyses. Nowadays, it is known that individual organisms have unique genome sequences and that differences between these sequences are the reason for genetic diversity. Furthermore, the biomolecular processes of living organisms are steered by genes and the interplay of their products. Perturbations in these systems often lead to disease. Thus, one of the major question in biomedical research is how genetic variations influence gene function, and how these affect underlying biological pathways and gene interaction networks. One of the most common sources of genetic diversity are single nucleotide variations (SNVs). So-called Genome Wide Association Studies (GWAS) as well as expression Quantitative Trait Locus (eQTL) studies intend to associate SNVs with e.g. disease related binary or quantitative traits. However, available methods are usually limited to statistical analyses and previous approaches to improve the interpretation of the respective results are often insufficient. The goal of this dissertation was the development of new visual analytical approaches to assist purely statistical methods in the identification, characterization and interpretation of SNVs. Genomic variations, especially SNVs, also play an important role in the immensely growing field of paleogenetics, where DNA of ancient origin is compared to modern DNA with the intention to gain insights into evolutionary history. In this dissertation, a computational pipeline for comparative NGS analyses of ancient and modern DNA samples has been described. Special attention was given to the read merging step, which is required to cope with the quality limitations inherent to ancient DNA (aDNA), in particular DNA fragmentation and nucleotide misincorporation. In addition, aDNA is usually only retrievable in low amounts and it is often contaminated with DNA of modern microorganisms. To solve this issue, a highly economical microarray-based DNA capturing strategy has been developed for the parallel detection and enrichment of aDNA from up to 100 different human pathogens

Statistical and Computational Methods for Analyzing and Visualizing Large-Scale Genomic Datasets

Advances in large-scale genomic data production have led to a need for better methods to process, interpret, and organize this data. Starting with raw sequencing data, generating results requires many complex data processing steps, from quality control, alignment, and variant calling to genome wide association studies (GWAS) and characterization of expression quantitative trait loci (eQTL). In this dissertation, I present methods to address issues faced when working with large-scale genomic datasets. In Chapter 2, I present an analysis of 4,787 whole genomes sequenced for the study of age-related macular degeneration (AMD) as a follow-up fine-mapping study to previous work from the International AMD Genomics Consortium (IAMDGC). Through whole genome sequencing, we comprehensively characterized genetic variants associated with AMD in known loci to provide additional insights on the variants potentially responsible for the disease by leveraging 60,706 additional controls. Our study improved the understanding of loci associated with AMD and demonstrated the advantages and disadvantages of different approaches for fine-mapping studies with sequence-based genotypes. In Chapter 3, I describe a novel method and a software tool to perform Hardy-Weinberg equilibrium (HWE) tests for structured populations. In sequence-based genetic studies, HWE test statistics are important quality metrics to distinguish true genetic variants from artifactual ones, but it becomes much less informative when it is applied to a heterogeneous and/or structured population. As next generation sequencing studies contain samples from increasingly diverse ancestries, we developed a new HWE test which addresses both the statistical and computational challenges of modern large-scale sequencing data and implemented the method in a publicly available software tool. Moreover, we extensively evaluated our proposed method with alternative methods to test HWE in both simulated and real datasets. Our method has been successfully applied to the latest variant calling QC pipeline in the TOPMed project. In Chapter 4, I describe PheGET, a web application to interactively visualize Expression Quantitative Trait Loci (eQTLs) across tissues, genes, and regions to aid functional interpretations of regulatory variants. Tissue-specific expression has become increasingly important for understanding the links between genetic variation and disease. To address this need, the Genotype-Tissue Expression (GTEx) project collected and analyzed a treasure trove of expression data. However, effectively navigating this wealth of data to find signals relevant to researchers has become a major challenge. I demonstrate the functionalities of PheGET using the newest GTEx data on our eQTL browser website at https://eqtl.pheweb.org/, allowing the user to 1) view all cis-eQTLs for a single variant; 2) view and compare single-tissue, single-gene associations within any genomic region; 3) find the best eQTL signal in any given genomic region or gene; and 4) customize the plotted data in real time. PheGET is designed to handle and display the kind of complex multidimensional data often seen in our post-GWAS era, such as multi-tissue expression data, in an intuitive and convenient interface, giving researchers an additional tool to better understand the links between genetics and disease.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162918/1/amkwong_1.pd

Identification of Biomarker Systems of Autism Spectrum Disorder and Uterine Cancer

Complex diseases and disorders pose a challenge to scientists due to their variable and often inconsistent genetic and environmental underpinnings across affected individuals. Because of this variability, large condition-specific datasets and corresponding analytical tools and approaches are being curated as resources to investigate potential genetic trends in complex diseases and disorders. In this Dissertation, I used DNA- and RNA-based resources to discover polygenic biosignatures associated with Autism Spectrum Disorder (ASD) or uterine cancer. To explore the intersection of small-effect common DNA variants and regulation in ASD, I discovered and analyzed trends in allelic associations at eQTLs within ASD-affected individuals. Association of eQTLs underlying any phenotype brings the genetic variation closer to biochemical mechanism leading to phenotypic expression. Uterine cancer was additionally investigated using gene expression profiles from normal and cancerous uterine tissue samples, from which gene co-expression networks and corresponding gene regulatory networks were built and further studied. The biomarker discoveries discussed here reflect the importance of dry lab resources and the potential they hold for future discovery

Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series

Background: The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus is caused by complex organ-specific cellular mechanisms contributing to impaired insulin secretion and insulin resistance. Methods: We used systematic metabotyping by 1H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualise shortest paths between metabolites and genes significantly associated with each genomic block. Results: Despite strong genomic similarities (95-99%) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific metabotypes (mQTL) and genome-wide expression traits (eQTL). Variation in key metabolites like glucose, succinate, lactate or 3-hydroxybutyrate, and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing shortest path length drove prioritization of biological validations by gene silencing. Conclusions: These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulations and to characterize novel functional roles for genes determining tissue-specific metabolism