INSIGHTS | P E R S P E C T I V E S
REFERENCES AND NOTES
1. See supplementary materials for details. 2. High Level Panel on Food Security, Biofuels and food security (Food and Agricultural Organization, Rome, 2013). 3. National Research Council, Renewable Fuel Standard: Potential Economic and Environmental Effects of U.S. Biofuel Policy (National Academies Press, Washington, DC, 2011). 4. U.K. Renewable Fuels Agency, The Gallagher Review of the Indirect Effects of Biofuels Production (Renewable Fuels Agency, London, 2008). 5. S. A. Decara et al., Land-Use Change and Environmental Consequences of Biofuels: A Quantitative Review of the Literature (Institut National de la Recherche Agronomique, Paris, 2012). 6. M. Finkbeiner, Biomass Bioenergy 62, 218 (2014). 7. D. Zilberman, G. Hochman, D. Rajagopal, AgBiol. Forum 13, 11 (2010). 8. High Level Panel on Food Security, Price volatility and food security (Food and Agricultural Organization, Rome, 2011) 9. A. Dorward, Food Security 4, 633 (2012). 10. M. Filipski, K. Covarrubia, in Agricultural Policies for Poverty Reduction, J. Brooks et al., Eds. (OECD, Paris, 2010). 11. CARB, Proposed regulation to implement the low carbon fuel standard, Volume I, Staff Report: Initial Statement of Reasons (California Air Resources Board, Sacramento, CA 2009). 12. R. Edwards, D. Mulligan, L. Marelli, Indirect land use change from increased biofuel demand: Comparison of models and results for marginal biofuels production from different feedstocks (European Commission Joint Research Centre, Ispra, Italy 2010). 13. U.S. EPA, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis, (U.S. Environmental Protection Agency, Washington, DC, 2010). 14. D. Laborde, Assessing the Land Use Change Consequences of European Biofuel Policies (International Food Policy Research Institute, Washington, DC, 2011) 15. T. D. Searchinger et al., Science 326, 527 (2009). 16. T. D. Searchinger, Environ. Res. Lttrs. 5, 024007 (2010). 17. S. Berry, Biofuel Policy and Empirical Inputs to GTAP Models, Report to the California Air Resources Board (CARB, Sacramento 2011). 18. R. Plevin, M. Delucchi, F. Creutzig, J. Ind. Ecol. 18, 73 (2014). 19. G. Hochman, D. Rajagopal, G. Timilsina, D. Zilberman, Biomass Bioenergy 68, 106 (2014). 20. Food and Agricultural Policy Research Institute, Elasticity Database (accessed August 12, 2014); www.fapri.iastate. edu/tools/elasticity.aspx 21. M. Roberts, W. Schlenker, Am. Econ. Rev. 103, 2265 (2013).
Exome sequencing identifies a gene that causes amyotrophic lateral sclerosis genome-wide association (examining genetic variants in a genome-wide manner across individuals for association with a trait), and family-based next-generation sequencing have each produced startling new phases of genetic discovery. The study by Cirulli et al. (1), reported on page 1436 of this issue, marks an early success in another phase of gene discovery: the application of exome sequencing in large case-control cohorts to identify genetic factors involved in complex disease. This is one of the first successes in this area and provides insights into amyotrophic lateral sclerosis (ALS), as well as broader lessons for disease gene discovery. The design of Cirulli et al.’s effort to identify new genes that associate with ALS was essentially that of a two-phase exome-wide association study. Several approaches were used in the initial stage, each centering on the identification of rare variants within the protein-coding regions of the genome
By Andrew B. Singleton and Bryan J. Traynor
A
principal tool in the development of etiology-based therapies is the identification of the genetic determinants of disease. The logic of this approach rests on the expectation that knowledge of the genetic variants underlying disease will enhance our understanding of the molecular pathogenesis of disease and reveal viable points for therapeutic intervention. Because of the general adoption of this paradigm, genetics has been a dominant force in disease investigation over the past 25 years. Success in this area has come in waves, each driven by new methods and technology. Linkage (identifying segments of the genome that are associated with given traits), positional sequencing (sequencing specific candidate genes based on their location),
ALS genetics Genetic discoveries in ALS, showing the percentage of cases each gene is involved in and the type of genetic analysis by which the gene was discovered [adapted from (7)]. TBK1 TUBA4A
Linkage and Sanger sequencing Second-generation sequencing in families Linkage, genome-wide association, and second-generation method Second-generation sequencing in cohorts
15
CHCHD10 MATR3
PFN1 C90RF72
UBGLN2
10
VCP OPTN FUS
5
TDP43
SOD1
ACKNOWLEDGMENTS
The authors thank the David and Lucile Packard Foundation for financial support.
0 1993
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Downloaded from www.sciencemag.org on March 26, 2015
www.sciencemag.org/content/347/6229/1420/suppl/DC1
For complex disease genetics, collaboration drives progress
sciencemag.org SCIENCE
27 MARCH 2015 • VOL 347 ISSUE 6229
Published by AAAS
CREDIT: ADAPTED FROM (7)
SUPPLEMENTARY MATERIALS
GENETICS
Percent of cases
of calories diverted to biofuels are not replaced (21). Regardless of model merits, biofuel policies are relying on estimates of reduction in food consumption to claim GHG benefits. Food reductions result not from a tailored tax on overconsumption or high-carbon foods but from broad global increases in crop prices (8, 9). If models overstate the food reductions, then they understate the GHGs. Policy-makers who do not wish to mitigate climate change in this way could exclude the GHG credit for these reductions from their GHG calculations. Modelers need to make the trade-offs transparent so that policymakers can consider whether to seek climate mitigation through less food. ■
(exomes) and a burden analysis aimed at prioritizing genes with an excess of variants in cases over controls. The authors focused on three different models for selecting rare variants from the discovery set of 2843 ALS cases and 4310 controls: investigating all nonsynonymous coding variants (when a nucleotide is substituted, thereby producing a different amino acid) and canonical splice variants; looking at nonsynonymous variants that are predicted to be damaging variants; and assessing loss-of-function variants. On the basis of these analyses, 51 genes were taken forward to a replication effort in an additional set of 1318 ALS cases and 2371 controls. Although an attempt was made to exclude ALS cases with known mutations, it is notable that the top hit in the discovery effort was SOD1, a gene encoding superoxide dismutase 1, which is known to contain mutations that cause ALS. Other genes previously associated with ALS were also associated with disease in the analysis, including TAR DNA BINDING PROTEIN (TARDBP), OPTINEURIN (OPTN), VALOSIN CONTAINING PROTEIN (VCP), and SPASTIC PARAPLEGIA (SPG11). Cirulli et al. performed a large number of analyses and identified a number of interesting genes potentially involved in risk for ALS. However, the most immediately important and compelling finding is the identification of a new association between ALS and variants in TBK1, which encodes a noncanonical IκB kinase family member, TANK-binding kinase 1. The authors report an overall excess of rare TBK1 variants in cases under the “dominant not benign” model (dominant inherited variants that are predicted to be damaging to protein function), with a 0.19% allele frequency in controls versus 1.1% in cases (combined discovery and replication P value = 3.63 × 10−11). Although these data intuitively suggest that TBK1 variants play a role in ~0.9% of the ALS cases examined in the study, it is important to recognize that this does not suggest that TBK1 mutation is sufficient to cause disease, nor does it explain the cause of 0.9% of ALS cases. A great deal of additional work is required to establish whether disease-linked variants in this gene are risk variants, or causal mutations, or both (2). From a functional perspective, the linkage of TBK1 to ALS is interesting, as it highlights the importance of autophagy and degradation of ubiquitinated proteins in motor neuron degeneration. TBK1 phosphorylates the proteins encoded by genes previously linked to ALS, OPTN and SEQUESTOSOME 1 (SQSTM1). Phosphorylation by Tbk1 enhances the ability of these proteins to Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD 20892, USA. E-mail: [email protected]
shepherd ubiquitinated proteins to autophagosomes for destruction. Vcp and ubiquilin 2 (Ubqln2), also encoded by ALS-linked genes, are involved in later stages of the same cellular pathway, reinforcing the long-held view that ubiquitin-proteasome and autophagy pathways are central to ALS pathogenesis. The genetics field has changed perhaps more than any other scientific discipline over the past two decades. This transformation has been focused not only on methodology, but also on a more fundamental shift in the way gene hunting is executed. Traditionally a highly competitive field, gene hunting has evolved into a collaborative endeavor in which consortia and open data sharing are central tenets. The type of collaboration typified by Cirulli et al. has become a requirement for success. Other examples include large collaborative efforts in Parkinson’s disease and Alzheimer’s disease (3, 4). Another key to the success of Cirulli et al. was their access to publicly available data. Reference data from the Exome Aggregation Consortium (5) and the 1000 Genomes Project (6) were key steps in their filtering approach. The availability of such control and population sequence data is an essential resource for progress in disease research, and one that promotes efficiency and collaboration.
“The study…marks an early success in…the application of exome sequencing… to identify genetic factors involved in complex disease.” As Cirulli et al. rightly point out, much remains to be done to more fully understand the genetics of ALS (see the figure). Their work opens at least one new avenue into the investigation of molecular pathogenesis of disease, linking the protein product of TBK1 with known ALS proteins. It is likely that their genetic data will also serve as the foundation for further gene discovery in ALS. Given this, it is particularly laudable that the authors have made individual-level exome sequence data rapidly available to the broader scientific community. ■ REFERENCES
1. 2. 3. 4. 5. 6. 7.
E. T. Cirulli et al., Science 347, 1436 (2015). A. Singleton, J. Hardy, Hum. Mol. Genet. 20, R158 (2011). M. A. Nalls et al., Nat. Genet. 46, 989 (2014). J. C. Lambert et al., Nat. Genet. 45, 1452 (2013). exac.broadinstitute.org www.1000genomes.org A. E. Renton, A. Chiò, B. J. Traynor, Nat. Neurosci. 17, 17 (2014). 10.1126/science.aaa9838
SCIENCE sciencemag.org
APPLIED OPTICS
Sorting out light Controlled interference can separate overlapping light beams for device functionality By David A. B. Miller
W
hen light beams become mixed up, can we sort them out again? Some cases are easy. The light from two flashlights on the other side of a room overlaps when it reaches us, but the lens in our eye separates them again as it constructs an image. By turning the flashlights on and off, we could also communicate two independent, spatial channels of information to different detectors at the back of our eyes. But light is readily scrambled by anything more complicated than clear air. Trying to image through a strong scatterer, such as biological tissue, rapidly becomes impossible with increasing sample thickness. Even in the near-perfect glass fibers of optical communications, the slightest bend in the fiber can mix the light beams. Mathematically, interference of light waves just involves adding and subtracting numbers (which is a linear process), so we can exploit the matrices of linear algebra. With effort, we can measure the optical system’s matrix (1) and then calculate the inverse matrix that would mathematically undo the mixing. At least, we can do so in principle. However, we did not know how to implement such a (inverse) matrix as a lossless optical component. We did not even know if we could make an arbitrary linear optical device. Now, however, different mathematical approaches (2–6), together with modern microfabrication, may offer a solution. The optics may even solve the problem itself, thereby avoiding the need for any calculations (5, 6). Historically, optical components were simple objects, like lenses and mirrors. Now, the lithography developed for electronics offers more sophisticated possibilities. Silicon photonics (7) and other integrated approaches enable highly functional, complex (8) waveguide circuits near the top of a plane surface. Subwavelength patterning enables novel, nanophotonic structures, including photonic crystals, 27 MARCH 2015 • VOL 347 ISSUE 6229
Published by AAAS
1423
For complex disease genetics, collaboration drives progress Andrew B. Singleton and Bryan J. Traynor Science 347, 1422 (2015); DOI: 10.1126/science.aaa9838
If you wish to distribute this article to others, you can order high-quality copies for your colleagues, clients, or customers by clicking here. Permission to republish or repurpose articles or portions of articles can be obtained by following the guidelines here. The following resources related to this article are available online at www.sciencemag.org (this information is current as of March 26, 2015 ): Updated information and services, including high-resolution figures, can be found in the online version of this article at: http://www.sciencemag.org/content/347/6229/1422.full.html A list of selected additional articles on the Science Web sites related to this article can be found at: http://www.sciencemag.org/content/347/6229/1422.full.html#related This article cites 5 articles, 1 of which can be accessed free: http://www.sciencemag.org/content/347/6229/1422.full.html#ref-list-1 This article appears in the following subject collections: Genetics http://www.sciencemag.org/cgi/collection/genetics
Science (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. Copyright 2015 by the American Association for the Advancement of Science; all rights reserved. The title Science is a registered trademark of AAAS.
Downloaded from www.sciencemag.org on March 26, 2015
This copy is for your personal, non-commercial use only.
REFERENCES AND NOTES
1. See supplementary materials for details. 2. High Level Panel on Food Security, Biofuels and food security (Food and Agricultural Organization, Rome, 2013). 3. National Research Council, Renewable Fuel Standard: Potential Economic and Environmental Effects of U.S. Biofuel Policy (National Academies Press, Washington, DC, 2011). 4. U.K. Renewable Fuels Agency, The Gallagher Review of the Indirect Effects of Biofuels Production (Renewable Fuels Agency, London, 2008). 5. S. A. Decara et al., Land-Use Change and Environmental Consequences of Biofuels: A Quantitative Review of the Literature (Institut National de la Recherche Agronomique, Paris, 2012). 6. M. Finkbeiner, Biomass Bioenergy 62, 218 (2014). 7. D. Zilberman, G. Hochman, D. Rajagopal, AgBiol. Forum 13, 11 (2010). 8. High Level Panel on Food Security, Price volatility and food security (Food and Agricultural Organization, Rome, 2011) 9. A. Dorward, Food Security 4, 633 (2012). 10. M. Filipski, K. Covarrubia, in Agricultural Policies for Poverty Reduction, J. Brooks et al., Eds. (OECD, Paris, 2010). 11. CARB, Proposed regulation to implement the low carbon fuel standard, Volume I, Staff Report: Initial Statement of Reasons (California Air Resources Board, Sacramento, CA 2009). 12. R. Edwards, D. Mulligan, L. Marelli, Indirect land use change from increased biofuel demand: Comparison of models and results for marginal biofuels production from different feedstocks (European Commission Joint Research Centre, Ispra, Italy 2010). 13. U.S. EPA, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis, (U.S. Environmental Protection Agency, Washington, DC, 2010). 14. D. Laborde, Assessing the Land Use Change Consequences of European Biofuel Policies (International Food Policy Research Institute, Washington, DC, 2011) 15. T. D. Searchinger et al., Science 326, 527 (2009). 16. T. D. Searchinger, Environ. Res. Lttrs. 5, 024007 (2010). 17. S. Berry, Biofuel Policy and Empirical Inputs to GTAP Models, Report to the California Air Resources Board (CARB, Sacramento 2011). 18. R. Plevin, M. Delucchi, F. Creutzig, J. Ind. Ecol. 18, 73 (2014). 19. G. Hochman, D. Rajagopal, G. Timilsina, D. Zilberman, Biomass Bioenergy 68, 106 (2014). 20. Food and Agricultural Policy Research Institute, Elasticity Database (accessed August 12, 2014); www.fapri.iastate. edu/tools/elasticity.aspx 21. M. Roberts, W. Schlenker, Am. Econ. Rev. 103, 2265 (2013).
Exome sequencing identifies a gene that causes amyotrophic lateral sclerosis genome-wide association (examining genetic variants in a genome-wide manner across individuals for association with a trait), and family-based next-generation sequencing have each produced startling new phases of genetic discovery. The study by Cirulli et al. (1), reported on page 1436 of this issue, marks an early success in another phase of gene discovery: the application of exome sequencing in large case-control cohorts to identify genetic factors involved in complex disease. This is one of the first successes in this area and provides insights into amyotrophic lateral sclerosis (ALS), as well as broader lessons for disease gene discovery. The design of Cirulli et al.’s effort to identify new genes that associate with ALS was essentially that of a two-phase exome-wide association study. Several approaches were used in the initial stage, each centering on the identification of rare variants within the protein-coding regions of the genome
By Andrew B. Singleton and Bryan J. Traynor
A
principal tool in the development of etiology-based therapies is the identification of the genetic determinants of disease. The logic of this approach rests on the expectation that knowledge of the genetic variants underlying disease will enhance our understanding of the molecular pathogenesis of disease and reveal viable points for therapeutic intervention. Because of the general adoption of this paradigm, genetics has been a dominant force in disease investigation over the past 25 years. Success in this area has come in waves, each driven by new methods and technology. Linkage (identifying segments of the genome that are associated with given traits), positional sequencing (sequencing specific candidate genes based on their location),
ALS genetics Genetic discoveries in ALS, showing the percentage of cases each gene is involved in and the type of genetic analysis by which the gene was discovered [adapted from (7)]. TBK1 TUBA4A
Linkage and Sanger sequencing Second-generation sequencing in families Linkage, genome-wide association, and second-generation method Second-generation sequencing in cohorts
15
CHCHD10 MATR3
PFN1 C90RF72
UBGLN2
10
VCP OPTN FUS
5
TDP43
SOD1
ACKNOWLEDGMENTS
The authors thank the David and Lucile Packard Foundation for financial support.
0 1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
Year
10.1126/science.1261221
1422
Downloaded from www.sciencemag.org on March 26, 2015
www.sciencemag.org/content/347/6229/1420/suppl/DC1
For complex disease genetics, collaboration drives progress
sciencemag.org SCIENCE
27 MARCH 2015 • VOL 347 ISSUE 6229
Published by AAAS
CREDIT: ADAPTED FROM (7)
SUPPLEMENTARY MATERIALS
GENETICS
Percent of cases
of calories diverted to biofuels are not replaced (21). Regardless of model merits, biofuel policies are relying on estimates of reduction in food consumption to claim GHG benefits. Food reductions result not from a tailored tax on overconsumption or high-carbon foods but from broad global increases in crop prices (8, 9). If models overstate the food reductions, then they understate the GHGs. Policy-makers who do not wish to mitigate climate change in this way could exclude the GHG credit for these reductions from their GHG calculations. Modelers need to make the trade-offs transparent so that policymakers can consider whether to seek climate mitigation through less food. ■
(exomes) and a burden analysis aimed at prioritizing genes with an excess of variants in cases over controls. The authors focused on three different models for selecting rare variants from the discovery set of 2843 ALS cases and 4310 controls: investigating all nonsynonymous coding variants (when a nucleotide is substituted, thereby producing a different amino acid) and canonical splice variants; looking at nonsynonymous variants that are predicted to be damaging variants; and assessing loss-of-function variants. On the basis of these analyses, 51 genes were taken forward to a replication effort in an additional set of 1318 ALS cases and 2371 controls. Although an attempt was made to exclude ALS cases with known mutations, it is notable that the top hit in the discovery effort was SOD1, a gene encoding superoxide dismutase 1, which is known to contain mutations that cause ALS. Other genes previously associated with ALS were also associated with disease in the analysis, including TAR DNA BINDING PROTEIN (TARDBP), OPTINEURIN (OPTN), VALOSIN CONTAINING PROTEIN (VCP), and SPASTIC PARAPLEGIA (SPG11). Cirulli et al. performed a large number of analyses and identified a number of interesting genes potentially involved in risk for ALS. However, the most immediately important and compelling finding is the identification of a new association between ALS and variants in TBK1, which encodes a noncanonical IκB kinase family member, TANK-binding kinase 1. The authors report an overall excess of rare TBK1 variants in cases under the “dominant not benign” model (dominant inherited variants that are predicted to be damaging to protein function), with a 0.19% allele frequency in controls versus 1.1% in cases (combined discovery and replication P value = 3.63 × 10−11). Although these data intuitively suggest that TBK1 variants play a role in ~0.9% of the ALS cases examined in the study, it is important to recognize that this does not suggest that TBK1 mutation is sufficient to cause disease, nor does it explain the cause of 0.9% of ALS cases. A great deal of additional work is required to establish whether disease-linked variants in this gene are risk variants, or causal mutations, or both (2). From a functional perspective, the linkage of TBK1 to ALS is interesting, as it highlights the importance of autophagy and degradation of ubiquitinated proteins in motor neuron degeneration. TBK1 phosphorylates the proteins encoded by genes previously linked to ALS, OPTN and SEQUESTOSOME 1 (SQSTM1). Phosphorylation by Tbk1 enhances the ability of these proteins to Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD 20892, USA. E-mail: [email protected]
shepherd ubiquitinated proteins to autophagosomes for destruction. Vcp and ubiquilin 2 (Ubqln2), also encoded by ALS-linked genes, are involved in later stages of the same cellular pathway, reinforcing the long-held view that ubiquitin-proteasome and autophagy pathways are central to ALS pathogenesis. The genetics field has changed perhaps more than any other scientific discipline over the past two decades. This transformation has been focused not only on methodology, but also on a more fundamental shift in the way gene hunting is executed. Traditionally a highly competitive field, gene hunting has evolved into a collaborative endeavor in which consortia and open data sharing are central tenets. The type of collaboration typified by Cirulli et al. has become a requirement for success. Other examples include large collaborative efforts in Parkinson’s disease and Alzheimer’s disease (3, 4). Another key to the success of Cirulli et al. was their access to publicly available data. Reference data from the Exome Aggregation Consortium (5) and the 1000 Genomes Project (6) were key steps in their filtering approach. The availability of such control and population sequence data is an essential resource for progress in disease research, and one that promotes efficiency and collaboration.
“The study…marks an early success in…the application of exome sequencing… to identify genetic factors involved in complex disease.” As Cirulli et al. rightly point out, much remains to be done to more fully understand the genetics of ALS (see the figure). Their work opens at least one new avenue into the investigation of molecular pathogenesis of disease, linking the protein product of TBK1 with known ALS proteins. It is likely that their genetic data will also serve as the foundation for further gene discovery in ALS. Given this, it is particularly laudable that the authors have made individual-level exome sequence data rapidly available to the broader scientific community. ■ REFERENCES
1. 2. 3. 4. 5. 6. 7.
E. T. Cirulli et al., Science 347, 1436 (2015). A. Singleton, J. Hardy, Hum. Mol. Genet. 20, R158 (2011). M. A. Nalls et al., Nat. Genet. 46, 989 (2014). J. C. Lambert et al., Nat. Genet. 45, 1452 (2013). exac.broadinstitute.org www.1000genomes.org A. E. Renton, A. Chiò, B. J. Traynor, Nat. Neurosci. 17, 17 (2014). 10.1126/science.aaa9838
SCIENCE sciencemag.org
APPLIED OPTICS
Sorting out light Controlled interference can separate overlapping light beams for device functionality By David A. B. Miller
W
hen light beams become mixed up, can we sort them out again? Some cases are easy. The light from two flashlights on the other side of a room overlaps when it reaches us, but the lens in our eye separates them again as it constructs an image. By turning the flashlights on and off, we could also communicate two independent, spatial channels of information to different detectors at the back of our eyes. But light is readily scrambled by anything more complicated than clear air. Trying to image through a strong scatterer, such as biological tissue, rapidly becomes impossible with increasing sample thickness. Even in the near-perfect glass fibers of optical communications, the slightest bend in the fiber can mix the light beams. Mathematically, interference of light waves just involves adding and subtracting numbers (which is a linear process), so we can exploit the matrices of linear algebra. With effort, we can measure the optical system’s matrix (1) and then calculate the inverse matrix that would mathematically undo the mixing. At least, we can do so in principle. However, we did not know how to implement such a (inverse) matrix as a lossless optical component. We did not even know if we could make an arbitrary linear optical device. Now, however, different mathematical approaches (2–6), together with modern microfabrication, may offer a solution. The optics may even solve the problem itself, thereby avoiding the need for any calculations (5, 6). Historically, optical components were simple objects, like lenses and mirrors. Now, the lithography developed for electronics offers more sophisticated possibilities. Silicon photonics (7) and other integrated approaches enable highly functional, complex (8) waveguide circuits near the top of a plane surface. Subwavelength patterning enables novel, nanophotonic structures, including photonic crystals, 27 MARCH 2015 • VOL 347 ISSUE 6229
Published by AAAS
1423
For complex disease genetics, collaboration drives progress Andrew B. Singleton and Bryan J. Traynor Science 347, 1422 (2015); DOI: 10.1126/science.aaa9838
If you wish to distribute this article to others, you can order high-quality copies for your colleagues, clients, or customers by clicking here. Permission to republish or repurpose articles or portions of articles can be obtained by following the guidelines here. The following resources related to this article are available online at www.sciencemag.org (this information is current as of March 26, 2015 ): Updated information and services, including high-resolution figures, can be found in the online version of this article at: http://www.sciencemag.org/content/347/6229/1422.full.html A list of selected additional articles on the Science Web sites related to this article can be found at: http://www.sciencemag.org/content/347/6229/1422.full.html#related This article cites 5 articles, 1 of which can be accessed free: http://www.sciencemag.org/content/347/6229/1422.full.html#ref-list-1 This article appears in the following subject collections: Genetics http://www.sciencemag.org/cgi/collection/genetics
Science (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. Copyright 2015 by the American Association for the Advancement of Science; all rights reserved. The title Science is a registered trademark of AAAS.
Downloaded from www.sciencemag.org on March 26, 2015
This copy is for your personal, non-commercial use only.
