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Via Slash Gear

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Google has made clear their intent on joining the Global Alliance for Genomics and Health, a worldwide organization dedicated to standards, policies, and technology for the greater good of human health. Google’s role in this group will be to contribute toward refining technology and evolving the health research ecosystem for the whole planet.

Google will also be submitting open-source projects based on a web-based API to "import, process, store, and search genomic data at scale." In doing so, Google is submitting a proposal for this "simple web-based API" alongside a full preview implementation. This implementation will be utilizing the API built on Google’s cloud infrastructure, and will include sample data from public datasets galore.

The Google Genomics API will focus on the following from the start:

• Focus on science, not servers and file formats
- Use simple web APIs to access data wherever it lives
- Let us manage the servers and disks
• Store genomic data securely
- Private data remains private, public data is available to the community anywhere
- Storage space expands to fit your research needs
• Process as much data as you need, all at once
- Import data for entire cohorts in parallel
- Search and slice data from many samples in a single query

At the moment, potential users are being granted access to the Genomics API through a access request process. This process is done through Google itself, but may one day be hosted by the Global Alliance for Genomics and Health.

Google suggests that they are at the beginning of a big change in the global health and healthcare environment, and asks that other Global Alliance for Genomics and Health members contact them to "share your ideas about how to bring data science and life science together."

Via PCWorld

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NEC is working on a suitcase-sized DNA analyzer, which it says will be able to process samples at the scene of a crime or disaster in as little as 25 minutes.

The company said it aims to launch the device globally in 2014, and sell it for around 10 million yen, or US$120,000. It will output samples that can be quickly matched via the growing number of DNA databases worldwide.

“At first we will target investigative organizations, like police,” said spokeswoman Marita Takahashi. “We will also push its use on victims of natural disasters, to quickly match samples from siblings and parents.”

NEC hopes to use research and software from its mature fingerprint and facial matching technology, which have been deployed in everyday devices such as smartphones and ATMs.

The company said that the need for cheaper and faster DNA testing became clear in the aftermath of the Tohoku earthquake and tsunami that devasted much of Japan’s northeast coastline last year, when authorities performed nearly 20,000 samples.

NEC pointed to growing databases such as CODIS (Combined DNA Index System) in the U.S. and a Japanese database of DNA samples.

The company said it is aiming to make the device usable for those with minimal training, requiring only a cotton swab or small blood sample. NEC aims to make a device that weighs around 35 kilograms, measuring 850 millimeters by 552mm by 240mm, about the size of a large suitcase. The unit will run on a 12V power source.

NEC said it will be able to complete three-stage analysis process using a “lab on a chip” process, a term for for technology that recreates lab processes on chip-sized components. The basic steps for analysis include extracting DNA from samples, amplifying the DNA for analysis, and then separating out the different DNA strands.

The current version of the analyzer takes about an hour for all three tasks, and NEC said it aims to lower that to 25 minutes.

NEC it is carrying out the development of the analyzer together with partners including Promega, a U.S. biotechnology firm, and is testing it with a police science research institute in Japan.

Via PLOS genetics

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Abstract

Inter-individual variation in facial shape is one of the most noticeable phenotypes in humans, and it is clearly under genetic regulation; however, almost nothing is known about the genetic basis of normal human facial morphology. We therefore conducted a genome-wide association study for facial shape phenotypes in multiple discovery and replication cohorts, considering almost ten thousand individuals of European descent from several countries. Phenotyping of facial shape features was based on landmark data obtained from three-dimensional head magnetic resonance images (MRIs) and two-dimensional portrait images. We identified five independent genetic loci associated with different facial phenotypes, suggesting the involvement of five candidate genes—PRDM16, PAX3, TP63, C5orf50, and COL17A1—in the determination of the human face. Three of them have been implicated previously in vertebrate craniofacial development and disease, and the remaining two genes potentially represent novel players in the molecular networks governing facial development. Our finding at PAX3 influencing the position of the nasion replicates a recent GWAS of facial features. In addition to the reported GWA findings, we established links between common DNA variants previously associated with NSCL/P at 2p21, 8q24, 13q31, and 17q22 and normal facial-shape variations based on a candidate gene approach. Overall our study implies that DNA variants in genes essential for craniofacial development contribute with relatively small effect size to the spectrum of normal variation in human facial morphology. This observation has important consequences for future studies aiming to identify more genes involved in the human facial morphology, as well as for potential applications of DNA prediction of facial shape such as in future forensic applications.

Introduction

The morphogenesis and patterning of the face is one of the most complex events in mammalian embryogenesis. Signaling cascades initiated from both facial and neighboring tissues mediate transcriptional networks that act to direct fundamental cellular processes such as migration, proliferation, differentiation and controlled cell death. The complexity of human facial development is reflected in the high incidence of congenital craniofacial anomalies, and almost certainly underlies the vast spectrum of subtle variation that characterizes facial appearance in the human population.

Facial appearance has a strong genetic component; monozygotic (MZ) twins look more similar than dizygotic (DZ) twins or unrelated individuals. The heritability of craniofacial morphology is as high as 0.8 in twins and families [1], [2], [3]. Some craniofacial traits, such as facial height and position of the lower jaw, appear to be more heritable than others [1], [2], [3]. The general morphology of craniofacial bones is largely genetically determined and partly attributable to environmental factors [4]–[11]. Although genes have been mapped for various rare craniofacial syndromes largely inherited in Mendelian form [12], the genetic basis of normal variation in human facial shape is still poorly understood. An appreciation of the genetic basis of facial shape variation has far reaching implications for understanding the etiology of facial pathologies, the origin of major sensory organ systems, and even the evolution of vertebrates [13], [14]. In addition, it is feasible to speculate that once the majority of genetic determinants of facial morphology are understood, predicting facial appearance from DNA found at a crime scene will become useful as investigative tool in forensic case work [15]. Some externally visible human characteristics, such as eye color [16]–[18] and hair color [19], can already be inferred from a DNA sample with practically useful accuracies.

In a recent candidate gene study carried out in two independent European population samples, we investigated a potential association between risk alleles for non-syndromic cleft lip with or without cleft palate (NSCL/P) and nose width and facial width in the normal population [20]. Two NSCL/P associated single nucleotide polymorphisms (SNPs) showed association with different facial phenotypes in different populations. However, facial landmarks derived from 3-Dimensional (3D) magnetic resonance images (MRI) in one population and 2-Dimensional (2D) portrait images in the other population were not completely comparable, posing a challenge for combining phenotype data. In the present study, we focus on the MRI-based approach for capturing facial morphology since previous facial imaging studies by some of us have demonstrated that MRI-derived soft tissue landmarks represent a reliable data source [21], [22].

In geometric morphometrics, there are different ways to deal with the confounders of position and orientation of the landmark configurations, such as (1) superimposition [23], [24] that places the landmarks into a consensus reference frame; (2) deformation [25]–[27], where shape differences are described in terms of deformation fields of one object onto another; and (3) linear distances [28], [29], where Euclidean distances between landmarks instead of their coordinates are measured. Rationality and efficacy of these approaches have been reviewed and compared elsewhere [30]–[32]. We briefly compared these methods in the context of our genome-wide association study (GWAS) (see Methods section) and applied them when appropriate.

We extracted facial landmarks from 3D head MRI in 5,388 individuals of European origin from Netherlands, Australia, and Germany, and used partial Procrustes superimposition (PS) [24], [30], [33] to superimpose different sets of facial landmarks onto a consensus 3D Euclidean space. We derived 48 facial shape features from the superimposed landmarks and estimated their heritability in 79 MZ and 90 DZ Australian twin pairs. Subsequently, we conducted a series of GWAS separately for these facial shape dimensions, and attempted to replicate the identified associations in 568 Canadians of European (French) ancestry with similar 3D head MRI phenotypes and additionally sought supporting evidence in further 1,530 individuals from the UK and 2,337 from Australia for whom facial phenotypes were derived from 2D portrait images.

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The full article@PLOS genetics

Via Technology Review

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The first human genome cost $3 billion to complete; now we can sequence the entire population of Chicago for the same price

The mythical "$1,000 genome" is almost upon us, said Jonathan Rothberg, CEO of sequencing technology company Ion Torrent, at MIT's Emerging Technology conference. If his prediction comes true, it will represent an astonishing triumph in rapid technological development. The rate at which genome sequencing has become more affordable is faster than Moore's law. (You can read a Q&A TR did with Rothberg earlier this year here, and a profile of his company here).

"By this time next year sequencing human genomes as fast and cheap as bacterial genome," said Rothberg. (Earlier, he'd commented that his company can now do an entire bacterial genome in about two hours.)

I was in the room on October 19 when he said it, and I would have thought it pure hubris were it not for Rothberg's incredible track record in this area, from founding successful previous-generation sequencing company 454 Life Sciences to recent breakthroughs made with the same technology he proposes will get us to the $1,000 genome.

This technology, called, called the Personal Genome Machine, is already being used to determinewhich mutations are present in the genomes of patients' cancers.

The Personal Genome Maker is already showing up in clinical labs, even doctors' offices

The key to this breakthrough, says Rothberg, is that the PGM does not rely on conventional wet chemistry to sequence DNA. Instead, it works almost entirely through conventional microchip technology, which means Ion Torrent is leveraging decades of investment in conventional transistors and chips.

So what's the age of the $1,000 genome look like? Until we know what more of those genes actually correlate with, for most of us it won't be so different from the present.

"Right now don't have very many correlations between those 3 billion base pairs [of the human genome] and outcomes or medicines," says Rothberg. He predicts it will take at least 10 years of clinical experiments with full genome sequencing to get us to the point where we can begin to unlock its value.

"And it will be 20 years before we understand cancer at same level as HIV and can come up with combinations of medicine [tailored] for each individual," says Rothberg.

You can watch a full video of his talk on Tech Review's EmTech site.

Via New York Times

By Christopher Capozziello

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