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.

/pdf/the-genome-analysis-toolkit-a-mapreduce-framework-for-4aygfa9b66.pdf

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

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.

Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker’s variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers. Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies 1‐8 . Because the effects of stratification vary in proportion to the number of samples 9 , stratification will be an increasing problem in the large-scale association studies of the future, which will analyze thousands of samples in an effort to detect common genetic variants of weak effect. The two prevailing methods for dealing with stratification are genomic control and structured association 9‐14 . Although genomic control and structured association have proven useful in a variety of contexts, they have limitations. Genomic control corrects for stratification by adjusting association statistics at each marker by a uniform overall inflation factor. However, some markers differ in their allele frequencies across ancestral populations more than others. Thus, the uniform adjustment applied by genomic control may be insufficient at markers having unusually strong differentiation across ancestral populations and may be superfluous at markers devoid of such differentiation, leading to a loss in power. Structured association uses a program such as STRUCTURE 15 to assign the samples to discrete subpopulation clusters and then aggregates evidence of association within each cluster. If fractional membership in more than one cluster is allowed, the method cannot currently be applied to genome-wide association studies because of its intensive computational cost on large data sets. Furthermore, assignments of individuals to clusters are highly sensitive to the number of clusters, which is not well defined 14,16 .

/pdf/principal-components-analysis-corrects-for-stratification-in-e6a1v8jsyd.pdf

Principal components analysis corrects for stratification in genome-wide association studies

SummaryA new sensitive and objective method for the turbidimetric assay of tyrosinase activity in frozen skin sections is described. The procedure involves the dispersion of both melanin and skin and hair keratin, from previously incubated sections, in hot molar Na2S and KOH. Turbidimetric readings are then obtained on the absolute scale of the Klett-Summerson photoelectric colorimeter. This method is immediately useful for determining order of effect, and, if sufficiently improved, might also be useful for comparing amounts of naturally occurring pigment in different skin and hair samples.

A turbidimetric assay for tyrosinase activity in frozen sections of mouse skin.

Conducting genetics-related research with populations that have historically experienced considerable harm and little benefit from genetics research poses unique challenges for understanding community-based perceptions of new genetic technologies. This article identifies challenges and strategies for collecting qualitative data on the perceptions of direct-to-consumer (DTC) Genetic Ancestry tests (GAT) among diverse Indigenous communities. Based on a 3-year project related to perceptions, attitudes, and values associated with genetic ancestry testing among diverse Indigenous communities in Oklahoma, the engagement process revealed specific opportunities to improve the process of qualitative data collection related to GAT, and more broadly, to conduct genetics-related research with Indigenous communities in culturally and methodologically appropriate ways. Priority areas include issues related to participant recruitment and tribal advisory boards, challenges of self-identification as a recruitment mechanism, and the necessity of including Indigenous researchers in all aspects of the research process.

/pdf/barriers-and-strategies-related-to-qualitative-research-on-50dw5w53v9.pdf

Barriers and Strategies Related to Qualitative Research on Genetic Ancestry Testing in Indigenous Communities

Being Comanche: A Social History of an American Indian Community

The production of DOPA by normal pigmented mammalian skin.

Concerns have been raised that the increase in popular interest in genetics may herald a new era within which racial inequities are seen as 'natural' or immutable. In the following study, we provide data from a nationally representative survey on how the US population perceives general ability, athleticism, and intellect being determined by race and/or genetics and whether they believe racial health inequities to be primarily the product of genetic or social factors. We find that self-described race is of primary importance in attributing general ability to race, increasing age is a significant factor in attributing athleticism and intellect to genes and race, and education is a significant factor in decreasing such racially and genetically deterministic views . Beliefs about the meaning of race are statistically significantly associated with respect to the perception of athletic abilities and marginally associated with the perception of racial health inequalities being either socially or genetically derived. Race, education, socioeconomic status, and concepts of race were frequently found to be multiplicative in their statistical effects. The persistent acceptance of a genetically and racially deterministic view of athleticism among the White and older population group is discussed in respect to its social impact, as is the high level of agreement that general abilities are determined by race among non-White respondents and those of lower socioeconomic status. We argue that these findings highlight that both biological and non-biological forms of understanding race continue to play a role into the politics of race and social difference within contemporary US society.

Morris W. Foster

Papers

A turbidimetric assay for tyrosinase activity in frozen sections of mouse skin.

Barriers and Strategies Related to Qualitative Research on Genetic Ancestry Testing in Indigenous Communities

Being Comanche: A Social History of an American Indian Community

The production of DOPA by normal pigmented mammalian skin.

Genes, Race, and Causation: US Public Perspectives About Racial Difference