Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

Summary: Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.

Availability: http://www.broad.mit.edu/mpg/haploview/

Contact: jcbarret@broad.mit.edu

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Haploview: analysis and visualization of LD and haplotype maps

Motivation: DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.

Availability: Freely available to academic users from: http://www.ub.edu/dnasp

Contact: [email protected]

DnaSP v5

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.

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

By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

/pdf/an-integrated-map-of-genetic-variation-from-1-092-human-4vxat9g1wd.pdf

An integrated map of genetic variation from 1,092 human genomes

Healthy by law? The influence of labor and employment law on workers' health.

High-throughput biology is data intensive, and stem cell research is no exception to this trend. Funders often require scientists to share the data generated by high-throughput methods, because sharing speeds scientific discovery and increases the benefit of public investments in science. When human data are involved, the benefits of sharing must be balanced against the risks of inappropriately disclosing sensitive, personal information. Historically, scientists anonymized data to protect the interests of people whose data were shared. However, recent development of computational methods for re-identifying people from anonymous data and empirical demonstrations of re-identification have led many commentators to question whether anonymization still provides adequate protection for people whose data are included in shared databases. Because of disclosure concerns, data sharing repositories control who can access sensitive human data and what the approved users can do with those data. Stem cell researchers who create such repositories must develop governance mechanisms that prevent harm to individuals whose data they share.

Repositories for Sharing Human Data in Stem Cell Research

It has become increasingly difficult for individuals to exercise meaningful control over the personal data they disclose to companies or to understand and track the ways in which that data is exchanged and used. These developments have led to an emerging consensus that existing privacy and data protection laws offer individuals insufficient protections against harms stemming from current data practices. However, an effective and ethically justified way forward remains elusive. To inform policy in this area, we propose the Ethical Data Practices framework. The framework outlines six principles relevant to the collection and use of personal data-minimizing harm, fairly distributing benefits and burdens, respecting autonomy, transparency, accountability, and inclusion-and translates these principles into action-guiding practical imperatives for companies that process personal data. In addition to informing policy, the practical imperatives can be voluntarily adopted by companies to promote ethical data practices.

Ethical Responsibilities for Companies That Process Personal Data.

Perry Payne argues that the health care system should encourage provision of whole genome sequencing (WGS) for most people in the near future. Payne's essay contains two distinct claims. One claim is that near-universal access to WGS would be beneficial both to individuals and to populations who, without it, could be on the losing end of widening health disparities. The second claim is that the preventive services provisions of the Patient Protection and Affordable Care Act (ACA) should be invoked to establish legal entitlements to WGS, without any patient cost sharing. We believe there are strong reasons to reject both of these claims. Indeed, the reasons that count against providing wide access to WGS are the very same reasons that undermine Payne's argument for providing WGS under the preventive services provisions of the ACA.

Pilar N. Ossorio

Papers

Healthy by law? The influence of labor and employment law on workers' health.

Repositories for Sharing Human Data in Stem Cell Research

Ethical Responsibilities for Companies That Process Personal Data.

Why we should not use the Affordable Care Act to encourage widespread whole genome sequencing.

Myth and Mystification