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Embracing polygenicity: a review of methods and tools for psychiatric genetics research

  • R. M. Maier (a1) (a2), P. M. Visscher (a1) (a2), M. R. Robinson (a2) (a3) and N. R. Wray (a1) (a2)


The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Author for correspondence: R. M. Maier, E-mail:


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Abraham, G, Kowalczyk, A, Zobel, J and Inouye, M (2013) Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease. Genetic Epidemiology 37, 184195.
Anttila, AV, Finucane, H, Bras, J, Duncan, L, Falcone, G, Gormley, P et al. (2016) Analysis of shared heritability in common disorders of the brain Brainstorm consortium. bioRxiv 48991.
Barbeira, A, Shah, KP, Torres, JM, Wheeler, HE, Torstenson, ES, Edwards, T et al. (2016) Metaxcan: summary statistics based gene-level association method infers accurate PrediXcan results. bioRxiv 45260.
Bjørngaard, JH, Gunnell, D, Elvestad, MB, Davey Smith, G, Skorpen, F, Krokan, H et al. (2013) The causal role of smoking in anxiety and depression: a Mendelian randomization analysis of the HUNT study. Psychological Medicine 43, 711719.
Brion, MJA, Shakhbazov, K and Visscher, PM (2013) Calculating statistical power in Mendelian randomization studies. International Journal of Epidemiology 42, 14971501.
Brown, BC, Ye, CJ, Price, AL and Zaitlen, N (2016) Transethnic genetic-correlation estimates from summary statistics. American Journal of Human Genetics 99, 7688.
Bulik-Sullivan, B, Finucane, HK, Anttila, V, Gusev, A, Day, FR, Loh, P-R et al. (2015a) An atlas of genetic correlations across human diseases and traits. Nature Genetics 47, 12361241.
Bulik-Sullivan, BK, Loh, P-R, Finucane, HK, Ripke, S, Yang, J, Patterson, N et al. (2015b) LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics 47, 291295.
Burgess, S, Butterworth, A and Thompson, SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic Epidemiology 37, 658665.
De Candia, TR, Lee, SH, Yang, J, Browning, BL, Gejman, PV, Levinson, DF et al. (2013) Additive genetic variation in schizophrenia risk is shared by populations of African and European descent. American Journal of Human Genetics 93, 463470.
Chang, CC, Chow, CC, Tellier, LC, Vattikuti, S, Purcell, SM and Lee, JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7.
Chanock, SJ, Manolio, T, Boehnke, M, Boerwinkle, E, Hunter, DJ, Thomas, G et al. (2007) Replicating genotype-phenotype associations. Nature 447, 655660.
Chatterjee, N, Shi, J and García-Closas, M (2016) Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nature Reviews Genetics 14210, 1420514210.
Claussnitzer, M, Dankel, SN, Kim, K-H, Quon, G, Meuleman, W, Haugen, C et al. (2015) FTO obesity variant circuitry and adipocyte browning in humans. The New England Journal of Medicine 373, 895907.
de Los Campos, G, Vazquez, AI, Fernando, R, Klimentidis, YC and Sorensen, D (2013) Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genetics 9, e1003608.
Dudbridge, F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genetics 9, e1003348.
Euesden, J, Lewis, CM and O'Reilly, PF (2015) PRSice: polygenic risk score software. Bioinformatics 31, 14661468.
Evans, DM, Brion, MJA, Paternoster, L, Kemp, JP, McMahon, G, Munafò, M et al. (2013) Mining the human phenome using allelic scores that index biological intermediates. PLoS Genetics 9, e1003919.
Evans, DM and Davey Smith, G (2015) Mendelian randomization: new applications in the coming age of hypothesis-free causality. Annual Review of Genomics and Human Genetics 16, 327350.
Farh, KK-H, Marson, A, Zhu, J, Kleinewietfeld, M, Housley, WJ, Beik, S et al. (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337343.
Ferreira, MAR, Jansen, R, Willemsen, G, Penninx, B, Bain, LM, Vicente, CT et al. (2017) Gene-based analysis of regulatory variants identifies 4 putative novel asthma risk genes related to nucleotide synthesis and signaling. Journal of Allergy and Clinical Immunology 139, 11481157.
Finucane, HK, Bulik-Sullivan, BK, Gusev, A, Trynka, G, Reshef, Y, Loh, P-R et al. (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature Genetics 47, 12281235.
Flint, J and Kendler, KS (2014) The genetics of major depression. Neuron 81, 484503.
Gage, SH, Hickman, M and Zammit, S (2016) Association between cannabis and psychosis: epidemiologic evidence. Biological Psychiatry 79, 549556.
Gage, SH, Jones, HJ, Burgess, S, Bowden, J, Davey Smith, G, Zammit, S et al. (2017) Assessing causality in associations between cannabis use and schizophrenia risk: a two-sample Mendelian randomization study. Psychological Medicine 47, 971980.
Gage, SH, Smith, GD, Zammit, S, Hickman, M and Munafò, MR (2013) Using Mendelian randomisation to infer causality in depression and anxiety research. Depression and Anxiety 30, 11851193.
Gamazon, ER, Wheeler, HE, Shah, KP, Mozaffari, SV, Aquino-Michaels, K, Carroll, RJ et al. (2015) A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics 47, 10911098.
Gratten, J (2016) Rare variants are common in schizophrenia. Nature Neuroscience 19, 14261428.
Gusev, A, Ko, A, Shi, H, Bhatia, G, Chung, W, Penninx, BWJH et al. (2016a) Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics 48, 245252.
Gusev, A, Mancuso, N, Finucane, HK, Reshef, Y, Song, L, Safi, A et al. (2016b) Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. bioRxiv 67355.
Han, B, Pouget, JG, Slowikowski, K, Stahl, E, Lee, CH, Diogo, D et al. (2016) A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat Genet 48, 803810.
Hartwig, FP, Bowden, J, Loret de Mola, C, Tovo-Rodrigues, L, Davey Smith, G and Horta, BL (2016) Body mass index and psychiatric disorders: a Mendelian randomization study. Scientific Reports 6, 32730.
Hayes, BJ, Visscher, PM and Goddard, ME (2009) Increased accuracy of artificial selection by using the realized relationship matrix. Genetics Research 91, 4760.
Hemani, G, Zheng, J, Wade, KH, Laurin, C, Elsworth, B, Burgess, S et al. (2016) MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. bioRxiv 078972.
Hohman, TJ, Dumitrescu, L, Cox, NJ, Jefferson, AL and For The Alzheimer's Neuroimaging Initiative (2017) Genetic resilience to amyloid related cognitive decline. Brain Imaging and Behavior 11, 401409.
Homer, N, Szelinger, S, Redman, M, Duggan, D, Tembe, W, Muehling, J et al. (2008) Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genetics 4, e1000167.
Inoshita, M, Numata, S, Tajima, A, Kinoshita, M, Umehara, H, Nakataki, M et al. (2016) A significant causal association between C-reactive protein levels and schizophrenia. Scientific Reports 6, 26105.
Jeste, SS and Geschwind, DH (2014) Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nature Reviews Neurology 10, 7481.
Jones, HJ, Stergiakouli, E, Tansey, KE, Hubbard, L, Heron, J, Cannon, M et al. (2016) Phenotypic manifestation of genetic risk for schizophrenia during adolescence in the general population. JAMA Psychiatry 73, 221228.
Joyce, PR (1984) Age of onset in bipolar affective disorder and misdiagnosis as schizophrenia. Psychological Medicine 14, 145149.
Kapur, S, Phillips, AG and Insel, TR (2012) Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Molecular Psychiatry 17, 11741179.
Keene, D, Price, C, Shun-Shin, MJ and Francis, DP (2014) Effect on cardiovascular risk of high density lipoprotein targeted drug treatments niacin, fibrates, and CETP inhibitors: meta-analysis of randomised controlled trials including 117,411 patients. BMJ (Clinical research ed.) 349, g4379.
Kendler, KS (1987) The impact of diagnostic misclassification on the pattern of familial aggregation and coaggregation of psychiatric illness. Journal of Psychiatric Research 21, 5591.
Kendler, KS (2016) The schizophrenia polygenic risk score. JAMA Psychiatry 73, 193.
Lee, SH, DeCandia, TR, Ripke, S, Yang, J, Sullivan, PF, Goddard, ME et al. (2012a) Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nature Genetics 44, 247250.
Lee, SH, Ripke, S, Neale, BM, Faraone, SV, Purcell, SM, Perlis, RH et al. (2013) Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nature Genetics 45, 984994.
Lee, SH, Wray, NR, Goddard, ME and Visscher, PM (2011) Estimating missing heritability for disease from genome-wide association studies. American Journal of Human Genetics 88, 294305.
Lee, SH, Yang, J, Goddard, ME, Visscher, PM and Wray, NR (2012b) Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics (Oxford, England) 28, 25402542.
Liley, J, Todd, JA and Wallace, C (2016) A method for identifying genetic heterogeneity within phenotypically defined disease subgroups. Nature Genetics 49, 310316.
Lo, M-T, Hinds, DA, Tung, JY, Franz, C, Fan, C-C, Wang, Y et al. (2016) Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nature Genetics 49, 152156.
Loh, P-R, Tucker, G, Bulik-Sullivan, BK, Vilhjálmsson, BJ, Finucane, HK, Salem, RM et al. (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nature Genetics 47, 284290.
Maier, R, Moser, G, Chen, G-B, Ripke, S, Coryell, W, Potash, JB et al. (2015) Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. The American Journal of Human Genetics 96, 283294.
Mancuso, N, Shi, H, Goddard, P, Kichaev, G, Gusev, A and Pasaniuc, B (2017) Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. The American Journal of Human Genetics 100, 473487.
McGrath, J, Welham, J, Scott, J, Varghese, D, Degenhardt, L, Hayatbakhsh, MR et al. (2010) Association between cannabis use and psychosis-related outcomes using sibling pair analysis in a cohort of young adults. Archives of General Psychiatry 67, 440447.
Meuwissen, THE, Hayes, BJ and Goddard, ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 18191829.
Meyer, F and Meyer, TD (2009) The misdiagnosis of bipolar disorder as a psychotic disorder: some of its causes and their influence on therapy. Journal of Affective Disorders 112, 174183.
Morris, AP (2011) Transethnic meta-analysis of genomewide association studies. Genetic Epidemiology 35, 809822.
Moser, G, Lee, SH, Hayes, BJ, Goddard, ME, Wray, NR and Visscher, PM (2015) Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genetics 11, 122.
Mullins, N, Power, RA, Fisher, HL, Hanscombe, KB, Euesden, J, Iniesta, R et al. (2016) Polygenic interactions with environmental adversity in the aetiology of major depressive disorder. Psychological Medicine 46, 759770.
Neale, BM and Sham, PC (2004) The future of association studies: gene-based analysis and replication. American Journal of Human Genetics 75, 353362.
Okbay, A, Baselmans, BML, De Neve, J-E, Turley, P, Nivard, MG, Fontana, MA et al. (2016) Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nature Genetics 48, 624633.
Pasaniuc, B and Price, AL (2016) Dissecting the genetics of complex traits using summary association statistics. Nature Reviews Genetics 18, 117127.
Pers, TH, Karjalainen, JM, Chan, Y, Westra, H-J, Wood, AR, Yang, J et al. (2015) Biological interpretation of genome-wide association studies using predicted gene functions. Nature Communications 6, 5890.
Peyrot, WJ, Boomsma, DI, Penninx, BWJH and Wray, NR (2016) Disease and polygenic architecture: avoid trio design and appropriately account for unscreened control subjects for common disease. American Journal of Human Genetics 98, 382391.
Peyrot, WJ, Milaneschi, Y, Abdellaoui, A, Sullivan, PF, Hottenga, JJ, Boomsma, DI et al. (2014) Effect of polygenic risk scores on depression in childhood trauma. The British Journal of Psychiatry: The Journal of Mental Science 205, 113119.
Pickrell, JK, Berisa, T, Liu, JZ, Ségurel, L, Tung, JY, Hinds, DA et al. (2016) Detection and interpretation of shared genetic influences on 42 human traits. Nature Genetics 48, 709717.
Power, RA, Steinberg, S, Bjornsdottir, G, Rietveld, CA, Abdellaoui, A, Nivard, MM et al. (2015) Polygenic risk scores for schizophrenia and bipolar disorder predict creativity. Nature Neuroscience 18, 953955.
Power, RA, Verweij, KJH, Zuhair, M, Montgomery, GW, Henders, AK, Heath, AC et al. (2014) Genetic predisposition to schizophrenia associated with increased use of cannabis. Molecular Psychiatry 19, 12011204.
Prins, BP, Abbasi, A, Wong, A, Vaez, A, Nolte, I, Franceschini, N et al. (2016) Investigating the causal relationship of C-reactive protein with 32 complex somatic and psychiatric outcomes: a large-scale cross-consortium Mendelian randomization study. PLoS Medicine 13, 129.
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MAR, Bender, D et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81, 559575.
Purcell, SM, Wray, NR, Stone, JL, Visscher, PM, O'Donovan, MC, Sullivan, PF et al. (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748752.
Ripke, S, Neale, BM, Corvin, A, Walters, JTR, Farh, K-H, Holmans, PA et al. (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421427.
Robinson, MR, Kleinman, A, Graff, M, Vinkhuyzen, AAE, Couper, D, Miller, MB et al. (2017) Genetic evidence of assortative mating in humans. Nature Publishing Group. Nature Human Behaviour 1, 16.
Sankararaman, S, Obozinski, G, Jordan, MI and Halperin, E (2009) Genomic privacy and limits of individual detection in a pool. Nature Genetics 41, 965967.
Sekar, A, Bialas, AR, de Rivera, H, Davis, A, Hammond, TR, Kamitaki, N et al. (2016) Schizophrenia risk from complex variation of complement component 4. Nature 530, 177183.
Shah, K, Wheeler, HE, Gamazon, ER, Nicolae, DL, Cox, NJ and Im, HK (2016) Genetic predictors of gene expression associated with risk of bipolar disorder. bioRxiv.
Shi, H, Kichaev, G and Pasaniuc, B (2016) Contrasting the genetic architecture of 30 complex traits from summary association data. The American Journal of Human Genetics 99, 139153.
Solovieff, N, Cotsapas, C, Lee, PH, Purcell, SM and Smoller, JW (2013) Pleiotropy in complex traits: challenges and strategies. Nature Reviews Genetics 14, 483495.
Speed, D, Hemani, G, Johnson, MR and Balding, DJ (2012) Improved heritability estimation from genome-wide SNPs. The American Journal of Human Genetics 91, 10111021.
Stahl, EA, Wegmann, D, Trynka, G, Gutierrez-Achury, J, Do, R, Voight, BF et al. (2012) Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nature Genetics 44, 483489.
Sullivan, PF, Daly, MJ and O'Donovan, M (2012) Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nature Reviews Genetics 13, 537551.
Taylor, AE, Burgess, S, Ware, JJ, Gage, SH, Richards, JB, Davey Smith, G et al. (2016) Investigating causality in the association between 25(OH)D and schizophrenia. Scientific Reports 6, 26496.
Vaucher, J, Keating, BJ, Lasserre, AM, Gan, W, Lyall, DM, Ward, J et al. (2017) Cannabis use and risk of schizophrenia: a Mendelian randomization study. Molecular Psychiatry.
Vilhjálmsson, BJ, Yang, J, Finucane, HK, Gusev, A, Lindström, S, Ripke, S et al. (2015) Modeling linkage disequilibrium increases accuracy of polygenic risk scores. The American Journal of Human Genetics 97, 576592.
Vinkhuyzen, AAE, Wray, NR, Yang, J, Goddard, ME and Visscher, PM (2013) Estimation and partition of heritability in human populations using whole-genome analysis methods. Annual Review of Genetics 47, 7595.
Visscher, PM, Brown, MA, McCarthy, MI and Yang, J (2012) Five years of GWAS discovery. American Journal of Human Genetics 90, 724.
Visscher, PM, Hemani, G, Vinkhuyzen, AAE, Chen, G-B, Lee, SH, Wray, NR et al. (2014) Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples. PLoS Genetics 10, e1004269.
Visscher, PM and Hill, WG (2009) The limits of individual identification from sample allele frequencies: theory and statistical analysis. PLoS Genetics 5, 16.
Voight, BF, Peloso, GM, Orho-Melander, M, Frikke-Schmidt, R, Barbalic, M, Jensen, MK et al. (2012) Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. The Lancet 380, 572580.
Wang, K, Li, M and Hakonarson, H (2010) Analysing biological pathways in genome-wide association studies. Nature Reviews Genetics 11, 843854.
Wray, N, Goddard, M and Visscher, P (2007) Prediction of individual genetic risk to disease from genome-wide association studies. Genome Research 17, 15201528.
Wray, NR, Lee, SH and Kendler, KS (2012) Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes. European Journal of Human Genetics 20, 668674.
Wray, NR, Lee, SH, Mehta, D, Vinkhuyzen, AAE, Dudbridge, F and Middeldorp, CM (2014) Research review: polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry 55, 10681087.
Yang, J, Bakshi, A, Zhu, Z, Hemani, G, Vinkhuyzen, AAE, Lee, SH et al. (2015a) Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nature Genetics 47, 11141120.
Yang, J, Bakshi, A, Zhu, Z, Hemani, G, Vinkhuyzen, AAE, Nolte, IM et al. (2015b) Genome-wide genetic homogeneity between sexes and populations for human height and body mass index. Human Molecular Genetics 24, 74457449.
Yang, J, Benyamin, B, McEvoy, BP, Gordon, S, Henders, AK, Nyholt, DR et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42, 565569.
Yang, J, Ferreira, T, Morris, AP, Medland, SE, Madden, PAF, Heath, AC et al. (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature Genetics 44, 369375.
Yang, J, Lee, SH, Goddard, ME and Visscher, PM (2011a) GCTA: a tool for genome-wide complex trait analysis. American Journal of Human Genetics 88, 7682.
Yang, J, Weedon, MN, Purcell, S, Lettre, G, Estrada, K, Willer, CJ et al. (2011b) Genomic inflation factors under polygenic inheritance. European Journal of Human Genetics 19, 807812.
Yang, J, Zaitlen, NA, Goddard, ME, Visscher, PM and Price, AL (2014) Advantages and pitfalls in the application of mixed-model association methods. Nature Genetics 46, 100106.
Zheng, J, Erzurumluoglu, AM, Elsworth, BL, Kemp, JP, Howe, L, Haycock, PC et al. (2017) LD hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics (Oxford, England) 33, 272279.
Zhou, X and Stephens, M (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods 11, 407409.
Zhu, Z, Zhang, F, Hu, H, Bakshi, A, Robinson, MR, Powell, JE et al. (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature Genetics 48, 481487.


Embracing polygenicity: a review of methods and tools for psychiatric genetics research

  • R. M. Maier (a1) (a2), P. M. Visscher (a1) (a2), M. R. Robinson (a2) (a3) and N. R. Wray (a1) (a2)


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