The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

https://cdr.lib.unc.edu/downloads/v692t8920

A global reference for human genetic variation.

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

/pdf/analysis-of-protein-coding-genetic-variation-in-60-706-43wjw7d23z.pdf

Analysis of protein-coding genetic variation in 60,706 humans

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

/pdf/the-uk-biobank-resource-with-deep-phenotyping-and-genomic-2fs3jmksld.pdf

The UK Biobank resource with deep phenotyping and genomic data

Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysi...

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

https://oar.princeton.edu/bitstream/88435/pr1v99g/1/Genetic_effects_gene_expression_2017.pdf

Genetic effects on gene expression across human tissues.

Accumulation of insoluble protein in cells is associated with aging and aging- related diseases; however, the roles of insoluble protein in these processes are uncertain. The nature and impact of changes to protein solubility during normal aging are less well under- stood. Using quantitative mass spectrometry, we identify 480 proteins that become insoluble during postmitotic aging in Saccharomyces cerevisiae and show that this ensemble of insolu- ble proteins is similar to those that accumulate in aging nematodes. SDS-insoluble protein is present exclusively in a nonquiescent subpopulation of postmitotic cells, indicating an asym- metrical distribution of this protein. In addition, we show that nitrogen starvation of young cells is sufficient to cause accumulation of a similar group of insoluble proteins. Although many of the insoluble proteins identified are known to be autophagic substrates, induction of macroautophagy is not required for insoluble protein formation. However, genetic or chemi- cal inhibition of the Tor1 kinase is sufficient to promote accumulation of insoluble protein. We conclude that target of rapamycin complex 1 regulates accumulation of insoluble proteins via mechanisms acting upstream of macroautophagy. Our data indicate that the accumulation of proteins in an SDS-insoluble state in postmitotic cells represents a novel autophagic cargo preparation process that is regulated by the Tor1 kinase.

Tor1 regulates protein solubility in Saccharomyces cerevisiae

Background
Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins.

/pdf/stop-using-just-go-a-multi-ontology-hypothesis-generation-2cx22m0ka7.pdf

STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation

Advanced statistical methods used to analyze high-throughput data (e.g. gene-expression assays) result in long lists of "significant genes." One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. This process, referred to as enrichment analysis, profiles a gene-set, and is relevant for and extensible to data analysis with other high-throughput measurement modalities such as proteomics, metabolomics, and tissue-microarray assays. With the availability of tools for automatic ontology-based annotation of datasets with terms from biomedical ontologies besides the GO, we need not restrict enrichment analysis to the GO. We describe, RANSUM - Rich Annotation Summarizer - which performs enrichment analysis using any ontology in the National Center for Biomedical Ontology's (NCBO) BioPortal. We outline the methodology of enrichment analysis, the associated challenges, and discuss novel analyses enabled by RANSUM.

An ontology-neutral framework for enrichment analysis

Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

This paper presents a joint effort between a group of computer scientists and bioinformaticians to take an important step towards a general big data platform for genome analysis pipelines. The key goals of this study are to develop a thorough understanding of the strengths and limitations of big data technology for genomic data analysis, and to identify the key questions that the research community could address to realize the vision of personalized genomic medicine. Our platform, called Gesall, is based on the new "Wrapper Technology" that supports existing genomic data analysis programs in their native forms, without having to rewrite them. To do so, our system provides several layers of software, including a new Genome Data Parallel Toolkit (GDPT), which can be used to "wrap" existing data analysis programs. This platform offers a concrete context for evaluating big data technology for genomics: we report on super-linear speedup and sublinear speedup for various tasks, as well as the reasons why a parallel program could produce different results from those of a serial program. These results lead to key research questions that require a synergy between genomics scientists and computer scientists to find solutions.

https://dl.acm.org/doi/pdf/10.1145/3035918.3064048

Uday S. Evani

Papers

Tor1 regulates protein solubility in Saccharomyces cerevisiae

STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation

An ontology-neutral framework for enrichment analysis

Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

Massively Parallel Processing of Whole Genome Sequence Data: An In-Depth Performance Study