Dispatch R755
to form multiple colour spaces? Perhaps the outputs are used for detection of targets having a specific spectral reflectance that can be compared to a ‘search image’ leading to a behavioural response as suggested by Thoen et al. [12]. What is needed are more data about the neural ‘analytical engine’ employed by the shrimp to extract useful information from the receptor outputs. Even without this, behavioural experiments can be designed to elucidate the functional significance of specific UV patterns within the 300 nm to 400 nm range. One must not overlook the possibility that the information from the UV channels is somehow integrated with that from the other spectral channels or even the polarization channels to release specific behaviours. Even with all that is known about these fascinating animals, there is still a plethora of information to be gleaned from mantis shrimp that may lead to new ways of analyzing the visual world and identifying those targets within it of adaptive significance.
From the point of view of the observer, it would also be useful to obtain hyperspectral images of the visual world of mantis shrimps with the kind of spectral resolution and bandwidth found in the shrimps. What is there to ‘see’ in the UV between 300 nm and 400 nm that would make six separate spectral channels adaptive? There may be secrets in the UV visual ‘world’ of the marine environment yet to be discovered. References 1. Cronin, T.W., Bok, M.J., Marshall, N.J., and Caldwell, R.L. (2014). Filtering and polychromatic vision in mantis shrimps: themes in visible and ultraviolet vision. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130032. 2. Marshall, N.J., Cronin, T.W., and Kleinlogel, S. (2007). Stomatopod eye structure and function: A review. Arthropod Struct. Dev. 36, 420–448. 3. Porter, M.L., Bok, M.J., Robinson, P.R., and Cronin, T.W. (2009). Molecular diversity of visual pigments in Stomatopoda (Crustacea). Visual Neurosci. 26, 255–265. 4. Cronin, T.W., and Marshall, N.J. (1989). Multiple spectral classes of photoreceptors in the retinas of gonodactyloid stomatopod crustaceans. J. Comp. Physiol. [A] 166, 261–275. 5. Cronin, T.W., and Marshall, N.J. (1989). A retina with at least ten spectral types of photoreceptors in a mantis shrimp. Nature 339, 137–140.
Genetics: Finding Genes for Schizophrenia New studies have substantially advanced our understanding of the genetic architecture of schizophrenia, but we are far from identifying the underlying mutations. We may require new approaches to understand the biological implications of insights into the genetics of psychiatric disease. Jonathan Flint1,* and Marcus R. Munafo`2 There is a view that genome-wide association studies (GWAS) have not been particularly helpful when applied to psychiatric diseases. It’s true, the score-card is remarkably patchy: for the commonest condition, major depression, which ranks among the top three causes of morbidity world-wide [1], there are no agreed loci that contribute to disease risk, while for schizophrenia there are now 108 [2], a far higher score than for many other diseases. Differences in genetic architecture (that is, the number of loci involved and their individual effect sizes) explains the disparity in success rates: it’s estimated that
finding one locus contributing to major depression will require a case-control study with more than 50,000 cases [3], whilst the first findings for schizophrenia emerged when 9,000 cases were genotyped [4]. Why genetic architecture differs so much between diseases is not clear, but the consequences are unarguable: genetic dissection of inflammatory bowel disease required only a few thousand cases [5], whereas genetic dissection of hypertension required tens of thousands [6]. Three new studies [2,7,8] throw new light on the genetic basis of schizophrenia with implications for our understanding of the genetic architecture of psychiatric disease (Figure 1).
6. Cronin, T.W., Marshall, N.J., and Caldwell, R.L. (2000). Spectral tuning and the visual ecology of mantis shrimps. Philos. Trans. R. Soc. Lond. B. 355, 1263–1267. 7. Marshall, J., and Oberwinkler, J. (1999). The colourful world of the mantis shrimp. Nature 401, 873–874. 8. Loew, E.R. (1995). Determinants of visual pigment spectral location and photoreceptor cell spectral sensitivity. In The Outer Retina, M.B.A. Djamgoz, S.N. Archer, and S. Vallerga, eds. (London: Chapman & Hall), pp. 57–78. 9. Bok, M.J., Porter, M.L., Place, A.R., and Cronin, T.W. (2014). Biological sunscreens tune polychromatic ultraviolet vision in mantis shrimp. Curr. Biol. 24, 1636–1642. 10. Shick, J.M., and Dunlap, W.C. (2002). Mycosporine-like amino acids and related gadusols: biosynthesis, accumulation, and UV-protective functions in aquatic organisms. Annu. Rev. Physiol. 64, 223–262. 11. Thorpe, A., Douglas, R.H., and Truscott, R. (1993). Spectral transmission and shortwave absorbing pigments in the fish lens—I. Phylogenetic distribution and identity. Vision Res. 33, 289–300. 12. Thoen, H.H., How, M.J., Chiou, T.-H., and Marshall, N.J. (2014). A different form of color vision in mantis shrimp. Science 343, 411–413.
Department of Biomedical Sciences, Cornell University, Ithaca, NY 14853, USA. E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2014.07.025
From Genetics to Biological Insight The commonly held justification for carrying out (expensive) GWAS of disease is that the mapping studies take the first steps towards the identification of genes, from which will proceed novel insights into disease pathogenesis. Nowhere is this more needed than for psychiatric diseases, where the underlying biology remains shrouded in mystery. One hope was that sequencing genes near or at GWAS loci would prove a gene’s candidacy: finding individuals with the disease who carried deleterious mutations would unequivocally show that the mutated gene was involved in the disease (although this would not identify the sequence variants responsible for the GWAS signal). This argument assumed that in some people disease is due to large effect mutations. Successful sequencing of exomes at loci contributing to type 1 diabetes [9] and Crohn’s disease [10] gave some credence to the view that causal mutations could be found for complex disease. For schizophrenia, the advent of population-scale sequencing opened
Current Biology Vol 24 No 16 R756
Figure 1. A complex picture. Drawing entitled ‘Irren-Anstalt Band-Hain’ by artist Adolf Wo¨lfli (1864–1930), who was diagnosed with schizophrenia and spent the last 35 years of his life in a Swiss institution.
the possibility of sequencing a large number of exomes and looking for the tell-tale signatures of DNA variants that inactivate genes. In particular, mutations found in a proband (the affected individual), but not in his or her unaffected parents (thus de novo mutations), appear to provide good evidence for a gene’s causal role. A few years ago, the first exome studies of schizophrenia indicated that such mutations existed. One study identified 15 de novo mutations, four of which were reported as nonsense mutations, from sequencing just 14 schizophrenia families [11]. The other identified 40 de novo mutations in 27 cases [12]. The following year, from sequencing 231 affected families, the number of loss-of-function events was found to be 2.8 times higher than in control family trios (8.7% compared to 2.9%) [13]. It seemed only a matter of time before unambiguous, highly penetrant mutations would be found, and the long-sought entrance into the biology of schizophrenia finally revealed. Earlier this year two papers [7,8] described the largest exome sequencing studies yet attempted for schizophrenia, one reporting results for 2,536 cases and 2,543 controls [8], the other for 623 trios (two parents and one affected offspring) [7]. Even though the first study reports data from only 2,500 genes (about 10% of the genome), this
is still an enormous advance. Yet, the authors of neither study go so far as to give us the names of genes that they regard as unequivocally implicated by highly penetrant mutations. Indeed the second paper (the 623 trios) fails to confirm an enrichment of de novo mutations in cases of schizophrenia. Enrichment is found, but only when analysis is restricted to sets of candidate genes (which is also true of the first paper, with its focus on 2,500 genes that were implicated by previous findings). What is going on here? Why don’t we have a schizophrenia gene yet? The short answer is that it is still not clear what the ground rules are. In fact, this discussion touches on a deep divide, between those who think biologically, and those who think statistically. We’ll caricature the two positions, just to make the distinction between them obvious. Biology and Statistics Biologically speaking, finding a mutation that has a barn-door obvious effect, such as something that stops transcription in its tracks, needs no statistical justification. This position holds that mutations ablating two copies of a gene will (almost) always have a phenotype. Whether a single mutation has a noticeable effect depends on whether the cell can
function with half the usual amount of functional protein. The bottom line is that, without worrying unduly why this might be so, finding inactivating mutations is a great way to get a handle on the biology of a phenotype. There are many instances where the approach has worked, leading to new insights into basic biological processes [14,15]. The alternative standpoint, statistically speaking this time, holds that an unbiased screen for genetic mutations should be just that — reliant solely on the statistical evidence for association, unsullied by any post hoc justifications. Statistical rigour is one reason why GWAS have been successful — GWAS users deploy a single statistical threshold to declare significance for association between a sequence variant and phenotype (P < 5 3 1028). Most of the variants that achieve this threshold are replicable and robust. There is a degree of frustration that the same is not happening with sequence analysis. In a commentary published this year, there is a complaint that ‘‘investigators should not simply assume that the presence of two or more independently occurring de novo mutations in the same gene within a sequenced cohort is definitive evidence of a causal role for that gene; such a threshold results in ever increasing numbers of false positives as the number of sequenced cases increases’’ [16]. The recommendation is to adopt ‘‘a conservative genome-wide significance threshold corresponding to this testing strategy is a Bonferronicorrected P value of 1.7 3 1026 (that is, 0.05 out of 30,000)’’. Which approach is correct here? To many human geneticists, one of the surprises of 2012 was the report that a healthy genome contains about one hundred loss of function variants (i.e., mutations predicted to abrogate gene function). Every one of us carries mutations that inactivate both copies of approximately twenty genes [17]. The explanation for why these mutations are found in people who, to all intents and purposes, are perfectly healthy, is almost certainly the presence of other genetic variants that compensate for the loss of gene function. Any scientist working with inbred organisms knows how much genetic background influences phenotypes associated with a mutation. Genetic background can determine whether a mutation has no
Dispatch R757
phenotype, or whether it acts in a dominant or recessive fashion. The recent systematic analysis of mouse knockouts (complete abrogation of gene function) reports that 35% (56 of 160) appeared completely normal [18]. In other words, even with the application of rigorous criteria to define a highly deleterious mutation, we can still find these supposedly pathogenic mutations in healthy people. The reluctance of schizophrenia researchers to claim they have found causal mutations is understandable. Just what will it take to get to the point of declaring victory? The scale of the problem is made clear in a recent discovery of protein-altering variants in SLC30A8, confirming that the gene is involved in type 2 diabetes. Analysis of about 150,000 individuals was needed to secure the finding [19]. We are nowhere near that number for schizophrenia; indeed, it’s possible we’ll never have the type of evidence that we have for genes involved in diabetes or inflammatory bowel disease. The flexibility and adaptability that is such a notable feature of human behaviour brings a freedom from genetic determination that presumably extends to behaviour in illness. While our understanding of the genetic architecture of behaviour is too limited to make definitive claims, there is a real possibility that the sought after large effect mutations may be of much smaller effect than is hoped for. New approaches may be needed to work up the biological implications of our hard won insights into the genetic basis of psychiatric disease.
References 1. Ustun, T.B., Ayuso-Mateos, J.L., Chatterji, S., Mathers, C., and Murray, C.J. (2004). Global burden of depressive disorders in the year 2000. Br. J. Psychiatry 184, 386–392. 2. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia associated genetic loci. Nature 511, 421–427. 3. Flint, J., and Kendler, K.S. (2014). The genetics of major depression. Neuron 81, 484–503. 4. Purcell, S.M., Wray, N.R., Stone, J.L., Visscher, P.M., O’Donovan, M.C., Sullivan, P.F., and Sklar, P. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752. 5. Wellcome Trust Case Control Consortium (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678. 6. Newton-Cheh, C., Johnson, T., Gateva, V., Tobin, M.D., Bochud, M., Coin, L., Najjar, S.S., Zhao, J.H., Heath, S.C., Eyheramendy, S., et al. (2009). Genome-wide association study identifies eight loci associated with blood pressure. Nat. Genet. 41, 666–676. 7. Fromer, M., Pocklington, A.J., Kavanagh, D.H., Williams, H.J., Dwyer, S., Gormley, P., Georgieva, L., Rees, E., Palta, P., Ruderfer, D.M., et al. (2014). De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184. 8. Purcell, S.M., Moran, J.L., Fromer, M., Ruderfer, D., Solovieff, N., Roussos, P., O’Dushlaine, C., Chambert, K., Bergen, S.E., Kahler, A., et al. (2014). A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190. 9. Nejentsev, S., Walker, N., Riches, D., Egholm, M., and Todd, J.A. (2009). Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389. 10. Rivas, M.A., Beaudoin, M., Gardet, A., Stevens, C., Sharma, Y., Zhang, C.K., Boucher, G., Ripke, S., Ellinghaus, D., Burtt, N., et al. (2011). Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat. Genet. 43, 1066–1073. 11. Girard, S.L., Gauthier, J., Noreau, A., Xiong, L., Zhou, S., Jouan, L., Dionne-Laporte, A., Spiegelman, D., Henrion, E., Diallo, O., et al. (2011). Increased exonic de novo mutation rate in individuals with schizophrenia. Nat. Genet. 43, 860–863. 12. Xu, B., Roos, J.L., Dexheimer, P., Boone, B., Plummer, B., Levy, S., Gogos, J.A., and
Evolution: Ctenophore Genomes and the Origin of Neurons Recent sequencing of ctenophore genomes opens a new era in the study of this unique and phylogenetically distant group. The presence of neurodevelopmental genes, pre- and postsynaptic modules, and transmitter molecules is consistent with a single origin of neurons. Heather Marlow and Detlev Arendt The origin of neurons and nervous systems is one of the most exciting questions in animal evolution. Neurons, electrically excitable cells that signal to target cells via synapses, are
found in three animal lineages: in the bilaterians — comprising vertebrates, insects, nematodes and other groups often found with ganglia, nerve cords and brains; in the cnidarians — polyps and jellyfish with nerve nets that cover the entire body; and in a third
13.
14.
15.
16.
17.
18.
19.
Karayiorgou, M. (2011). Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat. Genet. 43, 864–868. Xu, B., Ionita-Laza, I., Roos, J.L., Boone, B., Woodrick, S., Sun, Y., Levy, S., Gogos, J.A., and Karayiorgou, M. (2012). De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat. Genet. 44, 1365–1369. Nusslein-Volhard, C., and Wieschaus, E. (1980). Mutations affecting segment number and polarity in Drosophila. Nature 287, 795–801. Metzstein, M.M., Stanfield, G.M., and Horvitz, H.R. (1998). Genetics of programmed cell death in C. elegans: past, present and future. Trends Genet. 14, 410–416. MacArthur, D.G., Manolio, T.A., Dimmock, D.P., Rehm, H.L., Shendure, J., Abecasis, G.R., Adams, D.R., Altman, R.B., Antonarakis, S.E., Ashley, E.A., et al. (2014). Guidelines for investigating causality of sequence variants in human disease. Nature 508, 469–476. MacArthur, D.G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., Jostins, L., Habegger, L., Pickrell, J.K., Montgomery, S.B., et al. (2012). A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828. White, J.K., Gerdin, A.K., Karp, N.A., Ryder, E., Buljan, M., Bussell, J.N., Salisbury, J., Clare, S., Ingham, N.J., Podrini, C., et al. (2013). Genomewide generation and systematic phenotyping of knockout mice reveals new roles for many genes. Cell 154, 452–464. Flannick, J., Thorleifsson, G., Beer, N.L., Jacobs, S.B., Grarup, N., Burtt, N.P., Mahajan, A., Fuchsberger, C., Atzmon, G., Benediktsson, R., et al. (2014). Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363.
1Wellcome
Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. 2MRC Integrative Epidemiology Unit, UK Centre for Tobacco and Alcohol Studies, and School of Experimental Psychology, University of Bristol, BS8 1TU, UK. *E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2014.07.018
group that, until very recently, has received little attention — the enigmatic ctenophores, or ‘comb jellies’ (Figure 1A). The ctenophore nervous system is a nerve net (Figure 1B) with local aggregations of neurons, most pronounced around the apex of the animal [1,2] (Figure 1C). As the gelatinous body of the ctenophores resembles that of cnidarian jellyfish, many authors assumed that cnidarians and ctenophores are related (grouped as ‘coelenterates’ [3]). Yet, this view has been challenged by the very different way these animals move: while rhythmic muscle contractions propel the cnidarian medusae, the comb jellies
to form multiple colour spaces? Perhaps the outputs are used for detection of targets having a specific spectral reflectance that can be compared to a ‘search image’ leading to a behavioural response as suggested by Thoen et al. [12]. What is needed are more data about the neural ‘analytical engine’ employed by the shrimp to extract useful information from the receptor outputs. Even without this, behavioural experiments can be designed to elucidate the functional significance of specific UV patterns within the 300 nm to 400 nm range. One must not overlook the possibility that the information from the UV channels is somehow integrated with that from the other spectral channels or even the polarization channels to release specific behaviours. Even with all that is known about these fascinating animals, there is still a plethora of information to be gleaned from mantis shrimp that may lead to new ways of analyzing the visual world and identifying those targets within it of adaptive significance.
From the point of view of the observer, it would also be useful to obtain hyperspectral images of the visual world of mantis shrimps with the kind of spectral resolution and bandwidth found in the shrimps. What is there to ‘see’ in the UV between 300 nm and 400 nm that would make six separate spectral channels adaptive? There may be secrets in the UV visual ‘world’ of the marine environment yet to be discovered. References 1. Cronin, T.W., Bok, M.J., Marshall, N.J., and Caldwell, R.L. (2014). Filtering and polychromatic vision in mantis shrimps: themes in visible and ultraviolet vision. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130032. 2. Marshall, N.J., Cronin, T.W., and Kleinlogel, S. (2007). Stomatopod eye structure and function: A review. Arthropod Struct. Dev. 36, 420–448. 3. Porter, M.L., Bok, M.J., Robinson, P.R., and Cronin, T.W. (2009). Molecular diversity of visual pigments in Stomatopoda (Crustacea). Visual Neurosci. 26, 255–265. 4. Cronin, T.W., and Marshall, N.J. (1989). Multiple spectral classes of photoreceptors in the retinas of gonodactyloid stomatopod crustaceans. J. Comp. Physiol. [A] 166, 261–275. 5. Cronin, T.W., and Marshall, N.J. (1989). A retina with at least ten spectral types of photoreceptors in a mantis shrimp. Nature 339, 137–140.
Genetics: Finding Genes for Schizophrenia New studies have substantially advanced our understanding of the genetic architecture of schizophrenia, but we are far from identifying the underlying mutations. We may require new approaches to understand the biological implications of insights into the genetics of psychiatric disease. Jonathan Flint1,* and Marcus R. Munafo`2 There is a view that genome-wide association studies (GWAS) have not been particularly helpful when applied to psychiatric diseases. It’s true, the score-card is remarkably patchy: for the commonest condition, major depression, which ranks among the top three causes of morbidity world-wide [1], there are no agreed loci that contribute to disease risk, while for schizophrenia there are now 108 [2], a far higher score than for many other diseases. Differences in genetic architecture (that is, the number of loci involved and their individual effect sizes) explains the disparity in success rates: it’s estimated that
finding one locus contributing to major depression will require a case-control study with more than 50,000 cases [3], whilst the first findings for schizophrenia emerged when 9,000 cases were genotyped [4]. Why genetic architecture differs so much between diseases is not clear, but the consequences are unarguable: genetic dissection of inflammatory bowel disease required only a few thousand cases [5], whereas genetic dissection of hypertension required tens of thousands [6]. Three new studies [2,7,8] throw new light on the genetic basis of schizophrenia with implications for our understanding of the genetic architecture of psychiatric disease (Figure 1).
6. Cronin, T.W., Marshall, N.J., and Caldwell, R.L. (2000). Spectral tuning and the visual ecology of mantis shrimps. Philos. Trans. R. Soc. Lond. B. 355, 1263–1267. 7. Marshall, J., and Oberwinkler, J. (1999). The colourful world of the mantis shrimp. Nature 401, 873–874. 8. Loew, E.R. (1995). Determinants of visual pigment spectral location and photoreceptor cell spectral sensitivity. In The Outer Retina, M.B.A. Djamgoz, S.N. Archer, and S. Vallerga, eds. (London: Chapman & Hall), pp. 57–78. 9. Bok, M.J., Porter, M.L., Place, A.R., and Cronin, T.W. (2014). Biological sunscreens tune polychromatic ultraviolet vision in mantis shrimp. Curr. Biol. 24, 1636–1642. 10. Shick, J.M., and Dunlap, W.C. (2002). Mycosporine-like amino acids and related gadusols: biosynthesis, accumulation, and UV-protective functions in aquatic organisms. Annu. Rev. Physiol. 64, 223–262. 11. Thorpe, A., Douglas, R.H., and Truscott, R. (1993). Spectral transmission and shortwave absorbing pigments in the fish lens—I. Phylogenetic distribution and identity. Vision Res. 33, 289–300. 12. Thoen, H.H., How, M.J., Chiou, T.-H., and Marshall, N.J. (2014). A different form of color vision in mantis shrimp. Science 343, 411–413.
Department of Biomedical Sciences, Cornell University, Ithaca, NY 14853, USA. E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2014.07.025
From Genetics to Biological Insight The commonly held justification for carrying out (expensive) GWAS of disease is that the mapping studies take the first steps towards the identification of genes, from which will proceed novel insights into disease pathogenesis. Nowhere is this more needed than for psychiatric diseases, where the underlying biology remains shrouded in mystery. One hope was that sequencing genes near or at GWAS loci would prove a gene’s candidacy: finding individuals with the disease who carried deleterious mutations would unequivocally show that the mutated gene was involved in the disease (although this would not identify the sequence variants responsible for the GWAS signal). This argument assumed that in some people disease is due to large effect mutations. Successful sequencing of exomes at loci contributing to type 1 diabetes [9] and Crohn’s disease [10] gave some credence to the view that causal mutations could be found for complex disease. For schizophrenia, the advent of population-scale sequencing opened
Current Biology Vol 24 No 16 R756
Figure 1. A complex picture. Drawing entitled ‘Irren-Anstalt Band-Hain’ by artist Adolf Wo¨lfli (1864–1930), who was diagnosed with schizophrenia and spent the last 35 years of his life in a Swiss institution.
the possibility of sequencing a large number of exomes and looking for the tell-tale signatures of DNA variants that inactivate genes. In particular, mutations found in a proband (the affected individual), but not in his or her unaffected parents (thus de novo mutations), appear to provide good evidence for a gene’s causal role. A few years ago, the first exome studies of schizophrenia indicated that such mutations existed. One study identified 15 de novo mutations, four of which were reported as nonsense mutations, from sequencing just 14 schizophrenia families [11]. The other identified 40 de novo mutations in 27 cases [12]. The following year, from sequencing 231 affected families, the number of loss-of-function events was found to be 2.8 times higher than in control family trios (8.7% compared to 2.9%) [13]. It seemed only a matter of time before unambiguous, highly penetrant mutations would be found, and the long-sought entrance into the biology of schizophrenia finally revealed. Earlier this year two papers [7,8] described the largest exome sequencing studies yet attempted for schizophrenia, one reporting results for 2,536 cases and 2,543 controls [8], the other for 623 trios (two parents and one affected offspring) [7]. Even though the first study reports data from only 2,500 genes (about 10% of the genome), this
is still an enormous advance. Yet, the authors of neither study go so far as to give us the names of genes that they regard as unequivocally implicated by highly penetrant mutations. Indeed the second paper (the 623 trios) fails to confirm an enrichment of de novo mutations in cases of schizophrenia. Enrichment is found, but only when analysis is restricted to sets of candidate genes (which is also true of the first paper, with its focus on 2,500 genes that were implicated by previous findings). What is going on here? Why don’t we have a schizophrenia gene yet? The short answer is that it is still not clear what the ground rules are. In fact, this discussion touches on a deep divide, between those who think biologically, and those who think statistically. We’ll caricature the two positions, just to make the distinction between them obvious. Biology and Statistics Biologically speaking, finding a mutation that has a barn-door obvious effect, such as something that stops transcription in its tracks, needs no statistical justification. This position holds that mutations ablating two copies of a gene will (almost) always have a phenotype. Whether a single mutation has a noticeable effect depends on whether the cell can
function with half the usual amount of functional protein. The bottom line is that, without worrying unduly why this might be so, finding inactivating mutations is a great way to get a handle on the biology of a phenotype. There are many instances where the approach has worked, leading to new insights into basic biological processes [14,15]. The alternative standpoint, statistically speaking this time, holds that an unbiased screen for genetic mutations should be just that — reliant solely on the statistical evidence for association, unsullied by any post hoc justifications. Statistical rigour is one reason why GWAS have been successful — GWAS users deploy a single statistical threshold to declare significance for association between a sequence variant and phenotype (P < 5 3 1028). Most of the variants that achieve this threshold are replicable and robust. There is a degree of frustration that the same is not happening with sequence analysis. In a commentary published this year, there is a complaint that ‘‘investigators should not simply assume that the presence of two or more independently occurring de novo mutations in the same gene within a sequenced cohort is definitive evidence of a causal role for that gene; such a threshold results in ever increasing numbers of false positives as the number of sequenced cases increases’’ [16]. The recommendation is to adopt ‘‘a conservative genome-wide significance threshold corresponding to this testing strategy is a Bonferronicorrected P value of 1.7 3 1026 (that is, 0.05 out of 30,000)’’. Which approach is correct here? To many human geneticists, one of the surprises of 2012 was the report that a healthy genome contains about one hundred loss of function variants (i.e., mutations predicted to abrogate gene function). Every one of us carries mutations that inactivate both copies of approximately twenty genes [17]. The explanation for why these mutations are found in people who, to all intents and purposes, are perfectly healthy, is almost certainly the presence of other genetic variants that compensate for the loss of gene function. Any scientist working with inbred organisms knows how much genetic background influences phenotypes associated with a mutation. Genetic background can determine whether a mutation has no
Dispatch R757
phenotype, or whether it acts in a dominant or recessive fashion. The recent systematic analysis of mouse knockouts (complete abrogation of gene function) reports that 35% (56 of 160) appeared completely normal [18]. In other words, even with the application of rigorous criteria to define a highly deleterious mutation, we can still find these supposedly pathogenic mutations in healthy people. The reluctance of schizophrenia researchers to claim they have found causal mutations is understandable. Just what will it take to get to the point of declaring victory? The scale of the problem is made clear in a recent discovery of protein-altering variants in SLC30A8, confirming that the gene is involved in type 2 diabetes. Analysis of about 150,000 individuals was needed to secure the finding [19]. We are nowhere near that number for schizophrenia; indeed, it’s possible we’ll never have the type of evidence that we have for genes involved in diabetes or inflammatory bowel disease. The flexibility and adaptability that is such a notable feature of human behaviour brings a freedom from genetic determination that presumably extends to behaviour in illness. While our understanding of the genetic architecture of behaviour is too limited to make definitive claims, there is a real possibility that the sought after large effect mutations may be of much smaller effect than is hoped for. New approaches may be needed to work up the biological implications of our hard won insights into the genetic basis of psychiatric disease.
References 1. Ustun, T.B., Ayuso-Mateos, J.L., Chatterji, S., Mathers, C., and Murray, C.J. (2004). Global burden of depressive disorders in the year 2000. Br. J. Psychiatry 184, 386–392. 2. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia associated genetic loci. Nature 511, 421–427. 3. Flint, J., and Kendler, K.S. (2014). The genetics of major depression. Neuron 81, 484–503. 4. Purcell, S.M., Wray, N.R., Stone, J.L., Visscher, P.M., O’Donovan, M.C., Sullivan, P.F., and Sklar, P. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752. 5. Wellcome Trust Case Control Consortium (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678. 6. Newton-Cheh, C., Johnson, T., Gateva, V., Tobin, M.D., Bochud, M., Coin, L., Najjar, S.S., Zhao, J.H., Heath, S.C., Eyheramendy, S., et al. (2009). Genome-wide association study identifies eight loci associated with blood pressure. Nat. Genet. 41, 666–676. 7. Fromer, M., Pocklington, A.J., Kavanagh, D.H., Williams, H.J., Dwyer, S., Gormley, P., Georgieva, L., Rees, E., Palta, P., Ruderfer, D.M., et al. (2014). De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184. 8. Purcell, S.M., Moran, J.L., Fromer, M., Ruderfer, D., Solovieff, N., Roussos, P., O’Dushlaine, C., Chambert, K., Bergen, S.E., Kahler, A., et al. (2014). A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190. 9. Nejentsev, S., Walker, N., Riches, D., Egholm, M., and Todd, J.A. (2009). Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389. 10. Rivas, M.A., Beaudoin, M., Gardet, A., Stevens, C., Sharma, Y., Zhang, C.K., Boucher, G., Ripke, S., Ellinghaus, D., Burtt, N., et al. (2011). Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat. Genet. 43, 1066–1073. 11. Girard, S.L., Gauthier, J., Noreau, A., Xiong, L., Zhou, S., Jouan, L., Dionne-Laporte, A., Spiegelman, D., Henrion, E., Diallo, O., et al. (2011). Increased exonic de novo mutation rate in individuals with schizophrenia. Nat. Genet. 43, 860–863. 12. Xu, B., Roos, J.L., Dexheimer, P., Boone, B., Plummer, B., Levy, S., Gogos, J.A., and
Evolution: Ctenophore Genomes and the Origin of Neurons Recent sequencing of ctenophore genomes opens a new era in the study of this unique and phylogenetically distant group. The presence of neurodevelopmental genes, pre- and postsynaptic modules, and transmitter molecules is consistent with a single origin of neurons. Heather Marlow and Detlev Arendt The origin of neurons and nervous systems is one of the most exciting questions in animal evolution. Neurons, electrically excitable cells that signal to target cells via synapses, are
found in three animal lineages: in the bilaterians — comprising vertebrates, insects, nematodes and other groups often found with ganglia, nerve cords and brains; in the cnidarians — polyps and jellyfish with nerve nets that cover the entire body; and in a third
13.
14.
15.
16.
17.
18.
19.
Karayiorgou, M. (2011). Exome sequencing supports a de novo mutational paradigm for schizophrenia. Nat. Genet. 43, 864–868. Xu, B., Ionita-Laza, I., Roos, J.L., Boone, B., Woodrick, S., Sun, Y., Levy, S., Gogos, J.A., and Karayiorgou, M. (2012). De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat. Genet. 44, 1365–1369. Nusslein-Volhard, C., and Wieschaus, E. (1980). Mutations affecting segment number and polarity in Drosophila. Nature 287, 795–801. Metzstein, M.M., Stanfield, G.M., and Horvitz, H.R. (1998). Genetics of programmed cell death in C. elegans: past, present and future. Trends Genet. 14, 410–416. MacArthur, D.G., Manolio, T.A., Dimmock, D.P., Rehm, H.L., Shendure, J., Abecasis, G.R., Adams, D.R., Altman, R.B., Antonarakis, S.E., Ashley, E.A., et al. (2014). Guidelines for investigating causality of sequence variants in human disease. Nature 508, 469–476. MacArthur, D.G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., Jostins, L., Habegger, L., Pickrell, J.K., Montgomery, S.B., et al. (2012). A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828. White, J.K., Gerdin, A.K., Karp, N.A., Ryder, E., Buljan, M., Bussell, J.N., Salisbury, J., Clare, S., Ingham, N.J., Podrini, C., et al. (2013). Genomewide generation and systematic phenotyping of knockout mice reveals new roles for many genes. Cell 154, 452–464. Flannick, J., Thorleifsson, G., Beer, N.L., Jacobs, S.B., Grarup, N., Burtt, N.P., Mahajan, A., Fuchsberger, C., Atzmon, G., Benediktsson, R., et al. (2014). Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363.
1Wellcome
Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. 2MRC Integrative Epidemiology Unit, UK Centre for Tobacco and Alcohol Studies, and School of Experimental Psychology, University of Bristol, BS8 1TU, UK. *E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2014.07.018
group that, until very recently, has received little attention — the enigmatic ctenophores, or ‘comb jellies’ (Figure 1A). The ctenophore nervous system is a nerve net (Figure 1B) with local aggregations of neurons, most pronounced around the apex of the animal [1,2] (Figure 1C). As the gelatinous body of the ctenophores resembles that of cnidarian jellyfish, many authors assumed that cnidarians and ctenophores are related (grouped as ‘coelenterates’ [3]). Yet, this view has been challenged by the very different way these animals move: while rhythmic muscle contractions propel the cnidarian medusae, the comb jellies
