Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-29T00:27:44.180Z Has data issue: false hasContentIssue false
  • English
  • Français

The future of genomics for developmentalists

Published online by Cambridge University Press:  17 December 2013

Robert Plomin  and
Michael A. Simpson
Robert Plomin*
Affiliation:
King's College London
Michael A. Simpson
Affiliation:
King's College London
*
Address correspondence and reprint requests to: Robert Plomin, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK; E-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

The momentum of genomic science will carry it far into the future and into the heart of research on typical and atypical behavioral development. The purpose of this paper is to focus on a few implications and applications of these advances for understanding behavioral development. Quantitative genetics is genomic and will chart the course for molecular genomic research now that these two worlds of genetics are merging in the search for many genes of small effect. Although current attempts to identify specific genes have had limited success, known as the missing heritability problem, whole-genome sequencing will improve this situation by identifying all DNA sequence variations, including rare variants. Because the heritability of complex traits is caused by many DNA variants of small effect in the population, polygenic scores that are composites of hundreds or thousands of DNA variants will be used by developmentalists to predict children's genetic risk and resilience. The most far-reaching advance will be the widespread availability of whole-genome sequence for children, which means that developmentalists would no longer need to obtain DNA or to genotype children in order to use genomic information in research or in the clinic.

Type
Regular Articles
Information
Development and Psychopathology , Volume 25 , Issue 4pt2: Development and Psychopathology: A Vision Realized , November 2013 , pp. 1263 - 1278
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altmann, A., Weber, P., Bader, D., Preuss, M., Binder, E. B., & Muller-Myhsok, B. (2012). A beginners guide to SNP calling from high-throughput DNA-sequencing data. Human Genetics, 131, 1541–1554. doi:10.1007/s00439-012-1213-zGoogle Scholar
Bacanu, S.-A. (2012). On optimal gene-based analysis of genome scans. Genetic Epidemiology, 36, 333–339. doi:10.1002/gepi.21625Google Scholar
Bell, J. T., & Spector, T. D. (2011). A twin approach to unraveling epigenetics. Trends in Genetics, 27, 116–125. doi:10.1016/j.tig.2010.12.005Google Scholar
Belsky, D. W., Moffitt, T. E., Houts, R., Bennett, G. G., Biddle, A. K., Blumenthal, J. A., et al. (2012). Polygenic risk, rapid childhood growth, and the development of obesity evidence from a 4-decade longitudinal study. Archives of Pediatrics and Adolescent Medicine, 166, 515–521.CrossRefGoogle ScholarPubMed
Benjamin, D. J., Cesarini, D., van der Loos, M. J. H. M., Dawes, C. T., Koellinger, P. D., Magnusson, P. K. E., et al. (2012). The genetic architecture of economic and political preferences. Proceedings of the National Academy of Sciences, 109, 8026–8031. doi:10.1073/pnas.1120666109CrossRefGoogle ScholarPubMed
Benyamin, B., Pourcain, B., Davis, O. S., Davies, G., Hansell, N. K., Brion, M. J., et al. (2013). Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Molecular Psychiatry. Advance online publication. doi:10.1038/mp.2012.184Google Scholar
Bick, J., Naumova, O., Hunter, S., Barbot, B., Lee, M., Luthar, S. S., et al. (2012). Childhood adversity and DNA methylation of genes involved in the hypothalamus–pituitary–adrenal axis and immune system: Whole-genome and candidate-gene associations. Development and Psychopathology, 24, 1417–1425. doi:10.1017/S0954579412000806Google Scholar
Carlyle, B. C., Duque, A., Kitchen, R. R., Bordner, K. A., Coman, D., Doolittle, E., et al. (2012). Maternal separation with early weaning: A rodent model providing novel insights into neglect-associated developmental deficits. Development and Psychopathology, 24, 1401–1416. doi:10.1017/S095457941200079XGoogle Scholar
Chabris, C. F., Hebert, B. M., Benjamin, D. J., Beauchamp, J., Cesarini, D., van der Loos, M., et al. (2012). Most reported genetic associations with general intelligence are probably false positives. Psychological Science, 23, 1314–1323. doi:10.1177/0956797611435528CrossRefGoogle ScholarPubMed
Collins, F. S. (2010). The language of life: DNA and the revolution in personalized medicine. New York: Harper Collins.Google Scholar
Conrad, D. F., Pinto, D., Redon, R., Feuk, L., Gokcumen, O., Zhang, Y. J., et al. (2010). Origins and functional impact of copy number variation in the human genome. Nature, 464, 704–712. doi:10.1038/nature08516Google Scholar
Cooper, G. M., Coe, B. P., Girirajan, S., Rosenfeld, J. A., Vu, T. H., Baker, C., et al. (2011). A copy number variation morbidity map of developmental delay. Nature Genetics, 43, 838–844. doi:10.1038/ng.909Google Scholar
Cotsapas, C., Voight, B. F., Rossin, E., Lage, K., Neale, B. M., Wallace, C., et al. (2011). Pervasive sharing of genetic effects in autoimmune disease. PLoS Genetics, 7: e1002254. doi:10.1371/journal.pgen.1002254CrossRefGoogle ScholarPubMed
Cross-Disorder Group of the Psychiatric Genomics Consortium. (2013). Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis. Lancet, 381, 1371–1379. doi:10.1016/S0140-6736(12)62129-1Google Scholar
Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., et al. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry, 16, 996–1005. doi:10.1038/mp.2011.85Google Scholar
Deary, I. J., Yang, J., Davies, G., Harris, S. E., Tenesa, A., Liewald, D., et al. (2012). Genetic contributions to stability and change in intelligence from childhood to old age. Nature, 482, 212–215. doi:10.1038/nature10781Google Scholar
Deloukas, P., Kanoni, S., Willenborg, C., Farrall, M., Assimes, T., Thompson, J., et al. (2013). Large-scale association analysis identifies new risk loci for coronary artery disease. Nature Genetics, 45, 25–33. doi:10.1038/ng.2480CrossRefGoogle ScholarPubMed
Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLoS Genetics, 9, e1003348. doi:10.1371/journal.pgen.1003348CrossRefGoogle ScholarPubMed
Flint, J., DeFries, J. C., & Henderson, N. D. (2004). Little epistasis for anxiety-related measures in the DeFries strains of laboratory mice. Mammalian Genome, 15, 77–82. doi:10.1007/s00335-003-3033-xCrossRefGoogle ScholarPubMed
Geschwind, D. H. (2011). Genetics of autism spectrum disorders. Trends in Cognitive Sciences, 15, 409–416. doi:10.1016/j.tics.2011.07.003Google Scholar
Gibson, G. (2012). Rare and common variants: Twenty arguments. Nature Reviews Genetics, 13, 135–145. doi:10.1038/nrg3118Google Scholar
Gill, M. (2012). Developmental psychopathology: The role of structural variation in the genome. Development and Psychopathology, 24, 1319–1334. doi:10.1017/S0954579412000739CrossRefGoogle ScholarPubMed
Girirajan, S., Rosenfeld, J. A., Coe, B. P., Parikh, S., Friedman, N., Goldstein, A., et al. (2012). Phenotypic heterogeneity of genomic disorders and rare copy-number variants. New England Journal of Medicine, 367, 1321–1331. doi:10.1056/NEJMoa1200395CrossRefGoogle ScholarPubMed
Gordon, L., Joo, J. E., Powel, J. E., Ollikainen, M., Novakovic, B., Li, X., et al. (2012). Neonatal DNA methylation profile in human twins is specified by a complex interplay between intrauterine environmental and genetic factors, subject to tissue-specific influence. Genome Research, 22, 1395–1406. doi:10.1101/gr.136598.111Google Scholar
Gratten, J., Visscher, P. M., Mowry, B. J., & Wray, N. R. (2013). Interpreting the role of de novo protein-coding mutations in neuropsychiatric disease. Nature Genetics, 45, 234–238. doi:10.1038/ng.2555Google Scholar
Grigorenko, E. L., & Cicchetti, D. (2012). Genomic sciences for developmentalists: The current state of affairs. Development and Psychopathology, 24, 1157–1164. doi:10.1017/S0954579412000612Google Scholar
Guttmacher, A. E., McGuire, A. L., Ponder, B., & Stefansson, K. (2010). Personalized genomic information: Preparing for the future of genetic medicine. Nature Reviews Genetics, 11, 161–165. doi:10.1038/nrg2735CrossRefGoogle ScholarPubMed
Hackett, J. A., Sengupta, R., Zylicz, J. J., Murakami, K., Lee, C., Down, T. A., et al. (2013). Germline DNA demethylation dynamics and imprint erasure through 5-Hydroxymethylcytosine. Science, 339, 448–452. doi:10.1126/science.1229277Google Scholar
Harlaar, N., Butcher, L. M., Meaburn, E., Sham, P., Craig, I. W., & Plomin, R. (2005). A behavioural genomic analysis of DNA markers associated with general cognitive ability in 7-year-olds. Journal of Child Psychology and Psychiatry, 46, 1097–1107. doi:10.1111/j.1469-7610.2005.01515.xGoogle Scholar
Harold, G. T., Elam, K. K., Lewis, G., Rice, F., & Thapar, A. (2012). Interparental conflict, parent psychopathology, hostile parenting, and child antisocial behavior: Examining the role of maternal versus paternal influences using a novel genetically sensitive research design. Development and Psychopathology, 24, 1283–1295. doi:10.1017/S0954579412000703Google Scholar
Haworth, C. M. A., & Plomin, R. (2010). Quantitative genetics in the era of molecular genetics: Learning abilities and disabilities as an example. Journal of the American Academy of Child & Adolescent Psychiatry, 49, 783–793. doi:10.1016/j.jaac.2010.01.026Google Scholar
Heijmans, B. T., & Mill, J. (2012). Commentary: The seven plagues of epigenetic epidemiology. International Journal of Epidemiology, 41, 74–78. doi:10.1093/ije/dyr225Google Scholar
Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008). Data and theory point to mainly additive genetic variance for complex traits. PLoS Genetics, 4: e1000008. doi:10.1371/journal.pgen.1000008Google Scholar
Huang, J., Perlis, R. H., Lee, P. H., Rush, A. J., Fava, M., Sachs, G. S., et al. (2010). Cross-disorder genomewide analysis of schizophrenia, bipolar disorder, and depression. American Journal of Psychiatry, 167, 1254–1263. doi:10.1176/appi.ajp.2010.09091335Google Scholar
Hur, Y.-M., & Craig, J. M. (2013). Twin registries worldwide: An important resource for scientific research. Twin Research and Human Genetics, 16, 1–12. doi:10.1017/thg.2012.147Google Scholar
Huyghe, J. R., Jackson, A. U., Fogarty, M. P., Buchkovich, M. L., Stancakova, A., Stringham, H. M., et al. (2013). Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nature Genetics, 45, 197–201. doi:10.1038/ng.2507CrossRefGoogle ScholarPubMed
International Schizophrenia Consortium. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460, 748–752. doi:10.1038/nature08185Google Scholar
Jaffee, S. R., & Price, T. S. (2012). The implications of genotype–environment correlation for establishing causal processes in psychopathology. Development and Psychopathology, 24, 1253–1264. doi:10.1017/S0954579412000685Google Scholar
Kaminsky, Z. A., Tang, T., Wang, S. C., Ptak, C., Oh, G. H. T., Wong, A. H. C., et al. (2009). DNA methylation profiles in monozygotic and dizygotic twins. Nature Genetics, 41, 240–245. doi:10.1038/ng.286Google Scholar
Khoury, M. J., Yang, Q. H., Gwinn, M., Little, J. L., & Flanders, W. D. (2004). An epidemiologic assessment of genomic profiling for measuring susceptibility to common diseases and targeting interventions. Genetics in Medicine, 6, 38–47. doi:10.1097/01.GIM.0000105751.71430.79Google Scholar
Knopik, V. S., Maccani, M. A., Francazio, S., & McGeary, J. E. (2012). The epigenetics of maternal cigarette smoking during pregnancy and effects on child development. Development and Psychopathology, 24, 1377–1390. doi:10.1017/S0954579412000776Google Scholar
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., et al. (2010). Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature, 467, 832–838. doi:10.1038/nature09410Google Scholar
Lee, S. H., DeCandia, T. R., Ripke, S., Yang, J., The Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC-SCZ), The International Schizophrenia Consortium (ISC), et al. (2012). Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nature Genetics, 44, 247–250. doi:10.1038/ng.1108Google Scholar
Lee, S. H., Harold, D., Nyholt, D. R., Goddard, M. E., Zondervan, K. T., Williams, J., et al. (2013). Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis. Human Molecular Genetics, 22, 832–841. doi:10.1093/hmg/dds491CrossRefGoogle ScholarPubMed
Lee, S. H., Wray, N. R., Goddard, M. E., & Visscher, P. M. (2011). Estimating missing heritability for disease from genome-wide association studies. American Journal of Human Genetics, 88, 294–305. doi:10.1016/j.ajhg.2011.02.002Google Scholar
Lee, S. H., Yang, J., Goddard, M. E., Visscher, P. M., & Wray, N. R. (2012). Estimation of pleiotropy between complex diseases using SNP-derived genomic relationships and restricted maximum likelihood. Bioinformatics, 28, 2540–2542. doi:10.1093/bioinformatics/bts474Google Scholar
Lehne, B., & Schlitt, T. (2012). Breaking free from the chains of pathway annotation: De novo pathway discovery for the analysis of disease processes. Pharmacogenomics, 13, 1967–1978. doi:10.2217/pgs.12.170Google Scholar
Li, B. S., & Leal, S. M. (2008). Methods for detecting associations with rare variants for common diseases: Application to analysis of sequence data. American Journal of Human Genetics, 83, 311–321. doi:10.1016/j.ajhg.2008.06.024Google Scholar
Lichtenstein, P., Carlstrom, E., Rastam, M., Gillberg, C., & Anckarsater, H. (2010). The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. American Journal of Psychiatry, 167, 1357–1363. doi:10.1176/appi.ajp.2010.10020223Google Scholar
Lichtenstein, P., Yip, B. H., Bjork, C., Pawitan, Y., Cannon, T. D., Sullivan, P. F., et al. (2009). Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: A population-based study. Lancet, 373, 234–239. doi:10.1016/S0140-6736(09)60072-6CrossRefGoogle ScholarPubMed
Liu, J. Z., McRae, A. F., Nyholt, D. R., Medland, S. E., Wray, N. R., Brown, K. M., et al. (2010). A versatile gene-based test for genome-wide association studies. American Journal of Human Genetics, 87, 139–145. doi:10.1016/j.ajhg.2010.06.009Google Scholar
Liu, Y., Blackwood, D. H., Caesar, S., de Geus, E. J. C., Farmer, A., Ferreira, M. A. R., et al. (2011). Meta-analysis of genome-wide association data of bipolar disorder and major depressive disorder. Molecular Psychiatry, 16, 2–4. doi:10.1038/mp.2009.107Google Scholar
Lubke, G. H., Hottenga, J. J., Walters, R., Laurin, C., de Geus, E. J., Willemsen, G., et al. (2012). Estimating the genetic variance of major depressive disorder due to all single nucleotide polymorphisms. Biological Psychiatry, 72, 707–709. doi:10.1016/j.biopsych.2012.03.011Google Scholar
MacArthur, D. G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., et al. (2012). A systematic survey of loss-of-function variants in human protein-coding genes. Science, 335, 823–828. doi:10.1126/science.1215040CrossRefGoogle ScholarPubMed
Maitra, R. D., Kim, J., & Dunbar, W. B. (2012). Recent advances in nanopore sequencing. Electrophoresis, 33, 3418–3428. doi:10.1002/elps.201200272CrossRefGoogle ScholarPubMed
Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium. (2012). A mega-analysis of genome-wide association studies for major depressive disorder. Molecular Psychiatry, 18, 497–511. doi:10.1038/mp.2012.21Google Scholar
Malhotra, D., & Sebat, J. (2012). CNVs: Harbingers of a rare variant revolution in psychiatric genetics. Cell, 148, 1223–1241. doi:10.1016/j.cell.2012.02.039Google Scholar
Manolio, T. A. (2010). Genomewide association studies and assessment of the risk of disease. New England Journal of Medicine, 363, 166–176. doi:10.1056/NEJMra0905980Google Scholar
McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A., et al. (2008). Genome-wide association studies for complex traits: Consensus, uncertainty and challenges. Nature Reviews Genetics, 9, 356–369. doi:10.1038/nrg2344Google Scholar
McGrath, L. M., Weill, S., Robinson, E. B., Macrae, R., & Smoller, J. W. (2012). Bringing a developmental perspective to anxiety genetics. Development and Psychopathology, 24, 1179–1193. doi:10.1017/S0954579412000636Google Scholar
Mitchell, K. J. (2012). What is complex about complex disorders? Genome Biology, 13: 237. doi:10.1186/gb-2012-13-1-237Google Scholar
Monk, C., Spicer, J., & Champagne, F. A. (2012). Linking prenatal maternal adversity to developmental outcomes in infants: The role of epigenetic pathways. Development and Psychopathology, 24, 1361–1376. doi:10.1017/S0954579412000764Google Scholar
Morris, A. P., & Zeggini, E. (2010). An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genetic Epidemiology, 34, 188–193. doi:10.1002/gepi.20450Google Scholar
Naumova, O. Y., Palejev, D., Vlasova, N. V., Lee, M., Rychkov, S. Y., Babich, O. N., et al. (2012). Age-related changes of gene expression in the neocortex: Preliminary data on RNA-Seq of the transcriptome in three functionally distinct cortical areas. Development and Psychopathology, 24, 1427–1442. doi:10.1017/S0954579412000818Google Scholar
Neale, B. M., Medland, S. E., Ripke, S., Asherson, P., Franke, B., Lesch, K.-P., et al. (2010). Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 49, 884–897. doi:10.1016/j.jaac.2010.06.008Google Scholar
O'Reilly, P. F., Hoggart, C. J., Pomyen, Y., Calboli, F. C., Elliott, P., Jarvelin, M. R., et al. (2012). MultiPhen: Joint model of multiple phenotypes can increase discovery in GWAS. PLoS ONE, 7: e34861. doi:10.1371/journal.pone.0034861Google ScholarPubMed
Ott, J., Kamatani, Y., & Lathrop, M. (2011). Family-based designs for genome-wide association studies. Nature Reviews Genetics, 12, 465–474. doi:10.1038/nrg2989CrossRefGoogle ScholarPubMed
Pennisi, E. (2012). ENCODE project writes eulogy for junk DNA. Science, 337, 1159–1161. doi:10.1126/science.337.6099.1159Google Scholar
Pharoah, P. D. P., Antoniou, A., Bobrow, M., Zimmern, R. L., Easton, D. F., & Ponder, B. A. J. (2002). Polygenic susceptibility to breast cancer and implications for prevention. Nature Genetics, 31, 33–36. doi:10.1038/ng853Google Scholar
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., et al. (2010). Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature, 464, 768–772. doi:10.1038/nature08872Google Scholar
Plomin, R. (2013). Child development and molecular genetics: 14 years later. Child Development, 84, 104–120. doi:10.1111/j.1467-8624.2012.01757.xGoogle Scholar
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2013). Behavioral genetics (6th ed.). New York: Worth Publishers.Google Scholar
Plomin, R., Haworth, C. M. A., & Davis, O. S. P. (2009). Common disorders are quantitative traits. Nature Reviews Genetics, 10, 872–878. doi:10.1038/nrg2670Google Scholar
Plomin, R., Haworth, C. M. A., Meaburn, E. L., Price, T., Wellcome Trust Case Control Consortium 2, & Davis, O. S. P. (2013). Common DNA markers can account for more than half of the genetic influence on cognitive abilities. Psychological Science, 24, 562–568. doi:10.1177/0956797612457952CrossRefGoogle ScholarPubMed
Plomin, R., Hill, L., Craig, I., McGuffin, P., Purcell, S., Sham, P., et al. (2001). A genome-wide scan of 1842 DNA markers for allelic associations with general cognitive ability: A five-stage design using DNA pooling and extreme selected groups. Behavior Genetics, 31, 497–509. doi:10.1023/A:1013385125887Google Scholar
Plomin, R., & Kovas, Y. (2005). Generalist genes and learning disabilities. Psychological Bulletin, 131, 592–617. doi:10.1037/0033-2909.131.4.592Google Scholar
Psychiatric GWAS Consortium Bipolar Disorder Working Group. (2011). Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nature Genetics, 43, 977–983. doi:10.1038/ng.943Google Scholar
Ramanan, V. K., Shen, L., Moore, J. H., & Saykin, A. J. (2012). Pathway analysis of genomic data: Concepts, methods, and prospects for future development. Trends in Genetics, 28, 323–332. doi:10.1016/j.tig.2012.03.004Google Scholar
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., et al. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340, 1467–1471.Google Scholar
Rochman, B. (2012, December 13). ‘Want to know my future’? Parents grapple with delving into their kids' DNA. Time, 180(26).Google Scholar
Rucker, J. J. H., & McGuffin, P. (2012). Genomic structural variation in psychiatric disorders. Development and Psychopathology, 24, 1335–1344. doi:10.1017/S0954579412000740Google Scholar
Sahoo, T., Theisen, A., Rosenfeld, J. A., Lamb, A. N., Ravnan, J. B., Schultz, R. A., et al. (2011). Copy number variants of schizophrenia susceptibility loci are associated with a spectrum of speech and developmental delays and behavior problems. Genetics in Medicine, 13, 868–880. doi:10.1097/GIM.0b013e3182217a06Google Scholar
Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. (2011). Genome-wide association study identifies five new schizophrenia loci. Nature Genetics, 43, 969–976. doi:10.1038/ng.940Google Scholar
Scriver, C. R. (2007). The PAH gene, phenylketonuria, and a paradigm shift. Human Mutation, 28, 831–845. doi:10.1002/humu.20526Google Scholar
Siontis, K. C. M., Patsopoulos, N. A., & Ioannidis, J. P. A. (2010). Replication of past candidate loci for common diseases and phenotypes in 100 genome-wide association studies. European Journal of Human Genetics, 18, 832–837. doi:10.1038/ejhg.2010.26Google Scholar
Slatkin, M. (2009). Epigenetic inheritance and the missing heritability problem. Genetics, 182, 845–850. doi:10.1534/genetics.109.102798Google Scholar
So, H. C., Li, M. X., & Sham, P. C. (2011). Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genetic Epidemiology, 35, 447–456. doi:10.1002/gepi.20593Google Scholar
Speed, D., Hemani, G., Johnson, M. R., & Balding, D. J. (2012). Improved heritability estimation from genome-wide SNPs. American Journal of Human Genetics, 91, 1011–1021. doi:10.1016/j.ajhg.2012.10.010Google Scholar
Speliotes, E. K., Willer, C. J., Berndt, S. I., Monda, K. L., Thorleifsson, G., Jackson, A. U., et al. (2010). Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genetics, 42, 937–948. doi:10.1038/ng.686Google Scholar
Stein, J. L., Medland, S. E., Vasquez, A. A., Hibar, D. P., Senstad, R. E., Winkler, A. M., et al. (2012). Identification of common variants associated with human hippocampal and intracranial volumes. Nature Genetics, 44, 552–561. doi:10.1038/ng.2250Google Scholar
Stein, J. L., Parikshak, N. N., & Geschwind, D. H. (2013). Rare inherited variation in autism: Beginning to see the forest and a few trees. Neuron, 77, 209–211. doi:10.1016/j.neuron.2013.01.010CrossRefGoogle Scholar
Sullivan, P. (2012). Don't give up on GWAS. Molecular Psychiatry, 17, 2–3. doi:10.1038/mp.2011.94Google Scholar
Tabor, H. K., Risch, N. J., & Myers, R. M. (2002). Candidate-gene approaches for studying complex genetic traits: Practical considerations. Nature Reviews Genetics, 3, 391–397. doi:10.1038/nrg796Google Scholar
Topper, S., Ober, C., & Das, S. (2011). Exome sequencing and the genetics of intellectual disability. Clinical Genetics, 80, 117–126. doi:10.1111/j.1399-0004.2011.01720.xGoogle Scholar
Trzaskowski, M., Dale, P. S., & Plomin, R. (2013). No genetic influence for childhood behavior problems from DNA analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 52, 1048–1056.Google Scholar
Trzaskowski, M., Davis, O. S. P., DeFries, J. C., Yang, J., Visscher, P. M., & Plomin, R. (2013). DNA evidence for strong genome-wide pleiotropy of cognitive and learning abilities. behavior Genetics, 43, 267–273. doi:10.1007/s10519-013-9594-xCrossRefGoogle ScholarPubMed
Trzaskowski, M., Shakeshaft, N., & Plomin, R. (2013). Intelligence indexes generalist genes for cognitive abilities. Intelligence, 41, 560–565.Google Scholar
Trzaskowski, M., Yang, J., Visscher, P. M., & Plomin, R. (2013). DNA evidence for strong genetic stability and increasing heritability of intelligence from age 7 to 12. Molecular Psychiatry. Advance online publication. doi:10.1038/mp.2012.191Google ScholarPubMed
van Dongen, J., Slagboom, P. E., Draisma, H. H. M., Martin, N. G., & Boomsma, D. I. (2012). The continuing value of twin studies in the omics era. Nature Reviews Genetics, 13, 640–653. doi:10.1038/nrg3243CrossRefGoogle ScholarPubMed
Veltman, J. A., & Brunner, H. G. (2012). De novo mutations in human genetic disease. Nature Reviews Genetics, 13, 565–575. doi:10.1038/nrg3241Google Scholar
Vinkhuyzen, A. A. E., Pedersen, N. L., Yang, J., Lee, S. H., Magnusson, P. K. E., Iacono, W. G., et al. (2012). Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion. Translational Psychiatry, 2, e102. doi:10.1038/tp.2012.27Google Scholar
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012). Five years of GWAS discovery. American Journal of Human Genetics, 90, 7–24. doi:10.1016/j.ajhg.2011.11.029Google Scholar
Vrieze, S. I., Iacono, W. G., & McGue, M. (2012). Confluence of genes, environment, development, and behavior in a post genome-wide association study world. Development and Psychopathology, 24, 1195–1214. doi:10.1017/S0954579412000648Google Scholar
Vukcevic, D., Hechter, E., Spencer, C., & Donnelly, P. (2011). Disease model distortion in association studies. Genetic Epidemiology, 35, 278–290. doi:10.1002/gepi.20576Google Scholar
Weischenfeldt, J., Symmons, O., Spitz, F., & Korbel, J. O. (2013). Phenotypic impact of genomic structural variation: Insights from and for human disease. Nature Reviews Genetics, 14, 125–138. doi:10.1038/nrg3373Google Scholar
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. doi:10.1038/nature05911Google Scholar
Wellcome Trust Case Control Consortium. (2010). Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature, 464, 713–720. doi:10.1038/nature08979Google Scholar
Williams, N. M., Franke, B., Mick, E., Anney, R. J. L., Freitag, C. M., Gill, M., et al. (2012). Genome-wide analysis of copy number variants in attention deficit hyperactivity disorder: The role of rare variants and duplications at 15q13.3. American Journal of Psychiatry, 169, 195–204. doi:10.1176/appi.ajp.2011.11060822Google Scholar
Wong, C. C. Y., Caspi, A., Williams, B., Craig, I. W., Houts, R., Ambler, A., et al. (2010). A longitudinal study of epigenetic variation in twins. Epigenetics, 5, 516–526. doi:10.4161/epi.5.6.12226Google Scholar
Wong, C. C. Y., Meaburn, E. L., Ronald, A., Price, T. S., Jeffries, A. R., Schalkwyk, L. C., et al. (2013). Methylomic analysis of monozygotic twins discordant for autism spectrum disorder (ASD) and related behavioural traits. Molecular Psychiatry. Advance online publication. doi:10.1038/mp.2013.41Google ScholarPubMed
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 42, 565–569. doi:10.1038/ng.608Google Scholar
Yang, J. A., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: A tool for genome-wide complex trait analysis. American Journal of Human Genetics, 88, 76–82. doi:10.1016/j.ajhg.2010.11.011Google Scholar
Zaitlen, N., & Kraft, P. (2012). Heritability in the genome-wide association era. Human Genetics, 131, 1655–1664. doi:10.1007/s00439-012-1199-6Google Scholar
Zhernakova, A., Stahl, E. A., Trynka, G., Raychaudhuri, S., Festen, E. A., Franke, L., et al. (2011). Meta-analysis of genome-wide association studies in celiac disease and rheumatoid arthritis identifies fourteen non-HLA shared loci. PLoS Genetics, 7, e1002004. doi:10.1371/journal.pgen.1002004Google Scholar
Zhou, X., Carbonetto, P., & Stephens, M. (2013). Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genetics, 9, e1003264. doi:10.1371/journal.pgen.1003264Google Scholar