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

/pdf/haploview-analysis-and-visualization-of-ld-and-haplotype-1p9iyir38a.pdf

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

Basics of qualitative research: Grounded theory procedures and techniques

The CONSORT statement is used worldwide to improve the reporting of randomised controlled trials. Kenneth Schulz and colleagues describe the latest version, CONSORT 2010, which updates the reporting guideline based on new methodological evidence and accumulating experience. To encourage dissemination of the CONSORT 2010 Statement, this article is freely accessible on bmj.com and will also be published in the Lancet, Obstetrics and Gynecology, PLoS Medicine, Annals of Internal Medicine, Open Medicine, Journal of Clinical Epidemiology, BMC Medicine, and Trials.

/pdf/consort-2010-statement-updated-guidelines-for-reporting-4ae3ljk2ki.pdf

CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials

Not yet in sequence: Clinical, technical, ethical questions linger over personal genomics

To the editor: We read with interest Ken Chahine’s Business and Regulatory News article “Industry opposes genomic legislation” (Nat. Biotechnol. 20, 419, 2002). We were extremely disappointed that Chahine and the editors of Nature Biotechnology libelously published the innuendo and “beliefs” of the US Biotechnology Industry Organization (BIO; Washington, DC), suggesting our peer-reviewed work lacks “scientific rigor,” that our data were biased “by leading questions (among other things)” and that we violated unspecified internal review board (IRB) “guidelines.”BIO (along with Dr. Chahine) was provided on April 8 with a summary of data not included in our February 7 Nature article (Nature 415, 577–579, 2002). Neither BIO, Dr. Chahine, nor Nature Biotechnology requested copies of our interview guide, study protocol, and IRB approval letter. If they had, then they would have recognized that BIO’s disparaging statements about our study and about the ethics of our research practices were in reckless disregard of the truth. We were also troubled that Nature Biotechnology failed to include Chahine’s corporate title of vice president of business development and intellectual property for Avigen, Inc. The casual reader might easily be misled to think that the writer was an objective reporter instead of a biotechnology company executive. We believe publication of these unfounded statements and failure to disclose Chahine’s employment represents irresponsible journalism hardly befitting a Nature journal. Jon F. Merz, Center for Bioethics, Debra G.B. Leonard, Department of Pathology and Laboratory Medicine University of Pennsylvania, PA, 19104 Antigone G. Kriss, Finnegan, Henderson, Farabow, Garrett & Dunner, LLP, Washington, DC 20005 and Mildred K. Cho, Center for Biomedical Ethics Stanford University, Palo Alto, CA 94304 (merz@mail.med.upenn.edu)

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Industry opposes genomic legislation.

Big data and AI have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. "Digital simulacra" use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for in silico clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual. Nevertheless, digital simulacra mark a major milestone on a trajectory to embrace the epistemic culture of data science and a potential abandonment of medical epistemological concepts of causality and representation. In doing so, "data first" approaches potentially shift moral attention from actual patients and principles, such as equity, to simulated patients and patient data.

Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine.

Mejora en la calidad de la presentación de los ensayos controlados y de asignación aleatoria. La declaración CONSORT

From its inception, genomics has been a speculative endeavor, fixated on a far-off horizon that would deliver on the promise of targeted diagnostics and individualized therapeutics (Fortun 2008). More recently, the data-driven approach of precision medicine research furthers this trajectory by investigating individual differences in genes, environment, and lifestyle (Jooma et al. 2019). The substantial investment in genomic and precision medicine has begun to yield clinically useful interventions, yet questions about benefit and access create uncertainty about our ability to achieve equity goals. If the prediction by the National Human Genome Research Institute that “individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics” by 2030 is to be taken seriously, ethical, legal, and social implications (ELSI) research must engage with the meaning of equity and questions about whether and how genomic and precision medicine can achieve these goals in a geopolitical landscape characterized by extreme economic inequality and structural racism (Chancel and Piketty 2021; Green et al. 2020, 690; Yearby, Clark, and Figueroa 2022). Transparency about scientific practice and the extensive data collection and triangulation necessary for genomic and precision medicine will be critical to the production of trustworthy science (Lee et al. 2019). Sampling bias resulting in research datasets comprised mostly of samples from individuals of European ancestry (Popejoy and Fullerton 2016; Tsosie et al. 2021) means that peoples underrepresented in genomic research studies are more likely to receive non-informative or inaccurate genetic test results in the clinic (Burke 2021; Chapman-Davis et al. 2021; Landry and Rehm 2018; Manrai et al. 2016). Unequal access to genetic testing and interventions due to affordability, genetic literacy, and trust in the healthcare system have already undermined implementation of genomic applications (Khoury et al. 2022). Disparities and delays in access to genetic services for low income and minority patients in the U.S. are well documented (Chapman-Davis et al. 2021; Fraiman and Wojcik 2021; Gene Hallford et al. 2020; Hoskins et al. 2018; Omorodion et al. 2022; Shields, Burke, and Levy 2008; Wojcik et al. 2023). These barriers are likely to determine which Americans will be able to access and benefit from the substantial public investment in genomic and precision medicine and may undermine public trust in science (Lee 2021; Lee et al. 2019; Reardon et al. 2023). Fully realizing the promise of equity will require investment in the study of sociopolitical, economic, legal, regulatory, and environmental factors that parallels the public investment in genomics (Lee 2021). Leveraging multidisciplinary expertise and ELSI scholarship to address key questions should be paramount, including scholars and scholarship that can elucidate the roles of the built environment and health systems in facilitating or impeding equitable access, approaches for identifying and prioritizing the interests of communities, the creation of transparent public-private partnerships, and design of effective engagement to define the public good for genomic and precision medicine. This means centering the experiences of marginalized populations to create policies and practices that empower communities underrepresented in biomedical research to negotiate the terms of their research participation, ownership of their data, and the meaning of equitable benefit (Fox 2020; Tsosie et al. 2021). This special issue of the American Journal of Bioethics comprises ELSI research focused on equity and inclusion of diverse publics in genomic and precision medicine research. Articles in this issue represent work presented at the 5th ELSI Congress: Innovating for a Just and Equitable Future held June 1–3, 2022, a biennial conference supported by the National Human Genome Research Institute (NHGRI) at the National Institutes of Health (NIH) and organized by the ELSI Congress 2022 Organizing Committee.

Mildred K. Cho

Papers

Not yet in sequence: Clinical, technical, ethical questions linger over personal genomics

Industry opposes genomic legislation.

Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine.

Mejora en la calidad de la presentación de los ensayos controlados y de asignación aleatoria. La declaración CONSORT

Innovating for a Just and Equitable Future in Genomic and Precision Medicine Research