Category Archives: Transhuman News

By Jeff Hansel The Post-Bulletin, Austin MN

Speakers at Mayo Clinic'sIndividualizing Medicine conferenceoffered impressive examples of real-world genome-based treatment, and called for more research.

Michael Snyder, director for the Center for Genomics and Personalized Medicine at Stanford University, said Stanford researchers chose to study Snyder's own genome rather than recruit a test subject.

Speaking during this week's conference at Mayo Civic Center,Snyder said his entire genome has been sequenced. Whenever he gets a cold, he gets tested to see how the virus affects his genome and it does.

"Our health situation is really a product of our genome (a person's entire genetic makeup) as well as the food we eat, the various things we're exposed to in our environment," Snyder said.

Getting a full-genome sequence costs about $3,000 half the cost of buying a new hot tub, he said. That does not include $10,000 or so for scientists to make sense of results.

Still, genome-testing cost is decreasing rapidly and Snyder expects much of the population will soon be able to get a whole-genome analysis if desired.

Lots of ethical issues come up, such as how much of the information learned should be shared with the patient. If there's no cure for a disease and it's unclear how soon or if the patient will get it, should the patient be told he's at higher risk?

Mutations often do not affect a person's health directly, but some, such as combinations that increase the risk for Type 2 diabetes, might trigger disease when a person's body is exposed to disruption.

Stanford researchers, for example, demonstrated Snyder was at increased risk for Type 2 diabetes. He thought that odd, since he's not overweight and exercised a little.

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Genome testing could help individualize treatments

By Jeff Hansel The Post-Bulletin, Rochester MN

Speakers at Mayo Clinic'sIndividualizing Medicine conferenceoffered impressive examples of real-world genome-based treatment, and called for more research.

Michael Snyder, director for the Center for Genomics and Personalized Medicine at Stanford University, said Stanford researchers chose to study Snyder's own genome rather than recruit a test subject.

Speaking during this week's conference at Mayo Civic Center,Snyder said his entire genome has been sequenced. Whenever he gets a cold, he gets tested to see how the virus affects his genome and it does.

"Our health situation is really a product of our genome (a person's entire genetic makeup) as well as the food we eat, the various things we're exposed to in our environment," Snyder said.

Getting a full-genome sequence costs about $3,000 half the cost of buying a new hot tub, he said. That does not include $10,000 or so for scientists to make sense of results.

Still, genome-testing cost is decreasing rapidly and Snyder expects much of the population will soon be able to get a whole-genome analysis if desired.

Lots of ethical issues come up, such as how much of the information learned should be shared with the patient. If there's no cure for a disease and it's unclear how soon or if the patient will get it, should the patient be told he's at higher risk?

Mutations often do not impact a person's health directly. But some, such as combinations that increase the risk for Type 2 diabetes, might trigger disease when a person's body is exposed to disruption.

Stanford researchers, for example, demonstrated Snyder was at increased risk for Type 2 diabetes. He thought that odd, since he's not overweight and exercised a little.

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Would you get your genome tested?

Enlarge Courtesy of Beau Gunderson

Beau Gunderson's $999 genome test showed that he didn't inherit the gene for Alzheimer's, but he carries genes found in Olympic sprinters.

Beau Gunderson's $999 genome test showed that he didn't inherit the gene for Alzheimer's, but he carries genes found in Olympic sprinters.

Beau Gunderson's fascinated by what he might learn from his DNA.

"I'm curious about what makes me tick, essentially," says Gunderson, 29, who writes code for a Silicon Valley startup.

So Gunderson has signed up for every genetic test he's been able to afford. And he can't wait for the price of getting his entire genetic code his genome to drop to about $1,000, as many are predicting is imminent.

"Yeah, if the price does drop to a thousand bucks for example I might pay that. That's a good personal price point for me," Gunderson said.

So-called whole genome sequencing is already available for between about $4,000 and $10,000.

"The early adopters that are getting this done now are those who have this incredible curiosity about their genetic makeup, about their potential genetic destiny," said Jay Flatley, who heads Illumina Inc., of San Diego.

Illumina even recently started offering an iPad app that people can use to learn more about whole genome sequencing and their own genomes. In September, the company announced a new service that could deliver a genome in two weeks.

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Will Low-Cost Genome Sequencing Open 'Pandora's Box'?

CALGARY, Oct. 2, 2012 /CNW/ - Genome Alberta is pleased to be one of the sponsors of the Canadian Science Policy Conference 2012 which is coming to the West for the first time this November 5th - 7th. In particular we want to be the first to thank the Hon. Gary Goodyear, Minister of State for Science and Technology who will give the opening keynote address on Monday, November 5th at 8:45a, and the Hon. Stephen Khan, Alberta Minister of Enterprise and Advanced Education who will give the keynote luncheon speech on Tuesday, November 6th.

CSPC 2012 has lined up an impressive program with more than 90 speakers from industry, academia, the media and government. These include:

The keynote session: "Pulling Together: What is the appropriate division of labour between business, government, and the academy in advancing science-based innovation in Canada?" will feature a panel discussion with 3 leading Albertans who are also Honourary Co-Chairs of the National Science Policy Conference:

There will be 21 sessions during the conference, reflecting the four conference themes, submitted from across the country and internationally, including:

For the complete agenda please go tohttp://www.cspc2012.ca/glance.php and for descriptions of all the panel discussions see http://www.cspc2012.ca/paneldescriptions.php .

Genome Alberta is a strong supporter of the Canadian Science Policy Conference and of a strong provincial and national science policy that can produce the best in basic and applied research for the benefit of everyone. Canada was in the top 10 of the Scientific American Global Science Scorecard published this month, and a healthy discussion of science policy will ensure we maintain our strong ranking and be competitive on a global scale.

About Genome Alberta

Genome Alberta is apublicly funded organization that initiates, funds, and manages genomics research and partnerships. We are based in Calgary but lead projects around the province and participate in a variety of projects across the country. We are one of Canada's six Genome Centres and work closely with these centres to advance the science and application of genomics, metabolomics, and many other related 'omics'

You can find out more about Genome Alberta at http://genomealberta.ca , learn more about genomics on our blog pages at http://genomealberta.ca/blogs, follow us on Twitter as @GenomeAlberta, and check out our unique made-in-Alberta Genomics News site at http://GenOmicsNews.ca

SOURCE: Genome Alberta

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Genome Alberta Welcomes Alberta Minister of Enterprise and Advanced Education, Stephen Khan and Federal Minister of ...

ScienceDaily (Oct. 3, 2012) Investigators at Children's Mercy Hospitals and Clinics in Kansas City have just reported the first use of whole genome information for diagnosing critically ill infants. As reported in Science Translational Medicine, the team describes STAT-Seq, a whole genome sequencing approach -- from blood sample to returning results to a physician -- in about 50 hours. Currently, testing even a single gene takes six weeks or more.

Speed of diagnosis is most critical in acute care situations, as in a neonatal intensive care unit (NICU), where medical decision-making is made in hours not weeks. Using STAT-Seq, with consent from parents, the investigators diagnosed acutely ill infants from the hospital's NICU. By casting a broad net over the entire set of about 3,500 genetic diseases, STAT-Seq demonstrates for the first time the potential for genome sequencing to influence therapeutic decisions in the immediate needs of NICU patients.

"Up to one third of babies admitted to a NICU in the U.S. have genetic diseases," said Stephen Kingsmore, M.B. Ch.B., D.Sc., FRCPath, Director of the Center for Pediatric Genomic Medicine at Children's Mercy. "By obtaining an interpreted genome in about two days, physicians can make practical use of diagnostic results to tailor treatments to individual infants and children."

Genetic diseases affect about three percent of children and account for 15 percent of childhood hospitalizations. Treatments are currently available for more than 500 genetic diseases. In about 70 of these, such as infantile Pompe disease and Krabbe disease, initiation of therapy in newborns can help prevent disabilities and life-threatening illnesses.

STAT-Seq uses software that translates physician-entered clinical features in individual patients into a comprehensive set of relevant diseases. Developed at Children's Mercy, this software substantially automates identification of the DNA variations that can explain the child's condition. The team uses Illumina's HiSeq 2500 system, which sequences an entire genome at high coverage in about 25 hours.

Although further research is needed, STAT-Seq also has the potential to offer cost-saving benefits. "By shortening the time-to-diagnosis, we may markedly reduce the number of other tests performed and reduce delays to a diagnosis," said Kingsmore. "Reaching an accurate diagnosis quickly can help to shorten hospitalization and reduce costs and stress for families."

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The above story is reprinted from materials provided by Children's Mercy Hospital, via EurekAlert!, a service of AAAS.

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Fifty-hour whole genome sequencing provides rapid diagnosis for children with genetic disorders

Peering into the underlying causes of breast cancer, researchers and oncologists are increasingly turning to genome analysis of patients to identify the mutated genes that drive the leading cause of cancer fatalities for women across the world.

The last year has already yielded studies that will spur clinical trials and possibly new drug treatments within the next few years, offering hope to women with common, rare and aggressive forms of breast cancer.

Things now move so much faster, said Dr. Ian Krop, a professor at Harvard Medical School and oncologist at Dana-Farber Cancer Institute in Boston. Within the next year, were going to know whether these drugs work for these types of cancer. Then, it will be more than a year or two to be absolutely sure of the magnitude of the benefit. Were going to know pretty quickly.

At major cancer treatment centers such as Dana Farber, genome analysis of breast cancer patients has become the norm only in the last 12 months, fueled by the $$131.4 million spent on genome research, which Krop called the next big wave for treating and attacking cancers.

The genetic screening at Dana Farber tests tumor tissue for 471 mutations across 41 genes.

The most obvious benefit of genome analysis is that it gives doctors a breadth of detail theyve never had before -- which genes are involved in the cancer.

As weve started having the ability to look at the DNA of individual cancers, its become clear that breast cancers -- even though they make look the same under microscope -- are clearly different, said Krop. They are driven by different alterations in their DNA.

Dr. Matthew Meyerson, a pathologist at Dana Farber and a senior co-author of a breast cancer genome study released in June, described the results as groundbreaking.

We found a lot of genes that are mutated in breast cancer that were previously only found in leukemia, he said.

The discovery could open up a new treatment, using leukemia-fighting drugs to inhibit the cancer-driving protein called PI3 kinase.

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Genome analysis promises hope for breast cancer patients

Sampling and sequencing

The cultivated and wild rice accessions were all from large collections of rice accessions preserved at the China National Rice Research Institute in Hangzhou, China, and the National Institute of Genetics in Mishima, Japan. The accessions were selected on the basis of the germplasm database records of phenotypic data and sampling localities to maximize genetic and geographic diversity. The collection was maintained by selfing in the laboratories. For each accession, genomic DNA from a single plant was used for sequencing, and seeds derived from the same plant were used for following field trials. In total, the genomes of 1,083 O. sativa accessions, 446 O. rufipogon accessions and 15 accessions of outgroup species were sequenced on the Illumina Genome Analyzer IIx generating 73-bp (or 117-bp) paired-end reads, each to approximately onefold (for O. sativa accessions), twofold (for O. rufipogon accessions) or threefold (for outgroup species) coverage. The detailed information, including geographic origin and sequencing coverage of the rice accessions, was listed in Supplementary Tables 2, 7 and 8. Library construction and sequencing of these accessions were performed as described21. One representative accession of O. rufipogon, W1943, was sequenced on the Illumina HiSeq2000, generating 100-bp paired-end reads with 100-fold genome coverage. An amplification-free method of library preparation49 was used in deep sequencing of the rice accession, which reduced the incidence of duplicate sequences, thus facilitating genome assembly and variation analysis.

The paired-end reads of all the rice accessions were aligned against the rice reference genome (IRGSP 4.0) using the software Smalt (version 0.4) with the parameters of -pair 50, 700 and -mthresh 50. SNPs were called using the Ssaha Pileup package (version 0.5) with detailed procedure described previously21. Genotypes of the rice accessions, including 1,083 O. sativa accessions, 446 O. rufipogon accessions and 15 accessions of outgroup species, were further called at the SNP sites from the Ssaha Pileup outputs. The genotype calls in 15 accessions of outgroup species were used to determine the ancestral states of SNPs in O. sativa and O. rufipogon. SNPs in coding regions, which were defined based on the gene models in the RAP-DB (release 2), were then annotated to be synonymous or non-synonymous for calculating the non-synonymous/synonymous ratio and dN/dS ratio. The genotype data set of the 1,529 rice accessions (1,083 O. sativa accessions and 446 O. rufipogon accessions) was generated on the basis of the calls in each rice accession. Seven sets of genome sequences, which included bacterial-artificial-chromosome-based Sanger sequences and high-coverage resequencing data, were used to assess the accuracy of the genotype data sets (Supplementary Table 6). The wild rice accessions with sequencing coverage >9 were selected to investigate the heterozygosity based on the overlapped reads that were aligned onto the reference sequence. For each accession, the proportion of heterozygosity genotypes was calculated at the polymorphic sites (Supplementary Table 3).

The software Haploview was used to calculate linkage disequilibrium with default settings, using SNPs with information in 446 O. rufipogon accessions50. Pairwise r2 was calculated for all the SNPs and then averaged across the whole genome. The matrix of pairwise genetic distance derived from simple SNP-matching coefficients was used to construct phylogenetic trees using the software PHYLIP51 (version 3.66). The software TreeView and MEGA5 were used for visualizing the phylogenetic trees. Principal component analysis of the SNPs was performed using the software EIGENSOFT52. The sequence diversity statistics () and the population-differentiation statistics (FST) were computed using a 100-kb window. The value of was calculated for each group in O. rufipogon and O. sativa, respectively, and the ratio of in the full population (or each clade) of O. rufipogon to that in the full population (or corresponding subspecies) of O. sativa was used to detect selective sweeps. The genomic regions where both O. rufipogon and O. sativa show a low level of genetic diversity were excluded for further analysis. To adopt appropriate thresholds to reduce the false-positive rate but also retain true selection signals, thresholds were chosen on the basis of both whole-genome permutation tests and signals at known loci. Permutation tests were performed to estimate the genome-wide type I error rate and determine the threshold to call selective sweeps (see Supplementary Information section 2 for details)53. The method cross-population extended haplotype homozygosity (XP-EHH) was also tested for detecting selective sweeps using the software xpehh54 (http://hgdp.uchicago.edu/Software/) (Supplementary Fig. 20). The genetic distance between two clades was computed based on the matrix of pairwise genetic distance, where the distance of all pairs of accessions from the two clades were retrieved and averaged. A custom Perl script was developed to plot all O. rufipogon accessions, using the public geographic information of world borders from the Thematic Mapping data set (version 0.3). The computational simulations under different demographic scenarios were performed using the program SFS_CODE40.

For the O. rufipogon population, approximately five seeds for each accession from the collection of wild rice were germinated and planted in the experimental field (in Sanya, China at N 18.65, E 109.80) from March 2011. The leaf sheath colour was observed and scored directly and the tiller angle was measured for each plant. The mapping population of 210 backcross inbred lines (BILs) and 61 chromosome segment substitution lines (CSSLs) was derived from a cross between O. sativa ssp. indica cv. Guangluai-4 and O. rufipogon accession W1943. The BILs were developed by one generation of backcross to Guangluai-4 followed with six generations of self-fertilization. The CSSLs were developed by five generations of backcross to Guangluai-4 followed with three generations of self-fertilization. Phenotyping was conducted in the experimental field (in Shanghai, China at N 31.13, E 121.28) from May to October, 2011. The fifteen traits that we phenotyped for this study include germination rate, tiller angle, heading date, stigma colour, the degree of stigma exsertion, plant height, panicle length, the degree of shattering, awn length, grain number per panicle, grain length, grain width, grain weight per 1,000 grains, hull colour and pericarp colour. The degree of stigma exsertion was scored based on the observation of ~20 randomly sampled spikelets of each line, on a scale of 13 (no, incomplete or complete exertion). Seed germination rate was measured by using mature seeds which were placed in a plastic Petri dishes kept at 30C in the dark for 48h5. Other traits were phenotyped and scored as described previously21, 22, 29.

For the genotype data set in O. rufipogon, genotypes of 446 O. rufipogon accessions were called specifically at the ~5 million SNP sites that were polymorphic in the O. rufipogon population. In the panel for GWAS, only the SNPs that have a minor allele frequency (MAF) of more than 5% and contain genotype calls of more than 100 accessions were left for subsequent imputation. The k-nearest neighbour algorithm-based imputation method was used for inferring missing calls21. The specificity of the genotype data set before and after imputation was assessed using three sets of genome sequences (Supplementary Table 6). Association analysis was conducted using the compressed mixed linear model55. The top five principle components were used as fixed effects and the matrix of genetic distance was used to model the variancecovariance matrix of the random effect. Permutation tests were used to define the threshold of association signals of the GWAS in the wild rice population. A total of 20 permutation analyses were performed (10 independent permutation tests for each of the two traits, sheath colour and tiller angle), which resulted in two association signals with the thresholds we set53. Hence, there were an average of 0.1 false positives (that is, totally two false positives in 20 permutation tests) in a single whole-genome scanning analysis. Simulation tests were used to compare the performance of GWAS between the populations of cultivated and wild rice.

Genomic DNA of each line in the mapping population was sequenced on the Illumina Genome Analyzer IIx, each to approximately 0.5 coverage. Both parents of the population, Guangluai-4 and W1943, were sequenced with at least 20 genome coverage, in a previous work21 and in this study, respectively. SNP identification between parents was conducted as described previously41. Genotype calling, recombination breakpoint determination and bin map construction was performed using the software SEG-Map (http://www.ncgr.ac.cn/software/SEG/). QTL analysis of the fifteen traits was conducted with the composite interval mapping (CIM) method implemented in the software Windows QTL Cartographer56 (version 2.5) with a window size of 10cM and a step size of 2cM. QTL with LOD value higher than 3.5 were called, of which the location was described according to its LOD peak location. The phenotypic effect (r2) of each QTL was computed using Windows QTL Cartographer. QTLs located within selective sweep regions were further used to associate the selected regions with their biological functions. It needs to be noted that we adopted a stringent threshold in the QTL calling (LOD>3.5), and the genomic regions with LOD ranging from 2.5 to 3.5 may include many minor QTLs (the threshold was set to 2.5 in most studies).

The genome of W1943 was assembled by using a custom pipeline integrating Phusion2 (clustering the raw reads into different groups)57 and Phrap (then assembling all the reads in each group to generate contigs)58. The N50 length of the entire assembly was calculated for the initial contigs with small contigs of <200bp excluded. All the full-length complementary DNA sequences46 of W1943 were aligned with the final assembly of W1943 genome sequence using the software GMAP59 (version 6) with the parameters -K 15000 and -k 0.97. The resulting contigs from whole-genome de novo assembly were anchored to the rice reference genome sequence (IRGSP4.0) using the software MUMmer60 (version 3).

Gene models of the genome of the wild rice W1943 were predicted using the software Fgenesh that was set for a monocot model61 (version 2.0). The resulting proteome of W1943 was compared with protein sequences in Rice Genome Annotation Project (version 7.0) using BLASTP with a cutoff of a minimum of 95% identity. Sequence variants, including SNPs, indels and imbalanced substitutions, were called using the diffseq program in the EMBOSS package62. Indels of large size were called from the alignment results of MUMmer. Effects of the sequence variants were predicted according to the gene models of Nipponbare in the RAP-DB (release 2) across the rice genome. For indels in genic regions and SNPs with large effect around the domestication loci, the effects were mainly based on the reference gene models.

The sequence reads of 1,083 O. sativa accessions, 446 O. rufipogon accessions and 15 outgroup accessions were then aligned against assembled genome sequences of W1943 using the same parameters with those against the reference Nipponbare genome sequences. Genotypes of each accession were called at all sequence variant sites (including SNPs, indels and imbalanced substitutions that were detected from assembled sequences), based on the alignment outputs against the two genome sequences. The allele frequencies at the sequence variant sites were calculated for each clade of O. sativa and O. rufipogon. In each clade, variant sites with information of less than 10 accessions (less than 2 for the outgroups) were then excluded for computing allele frequencies, namely no data available.

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A map of rice genome variation reveals the origin of cultivated rice