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1. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Author

Howe, LJ, Nivard, MG, Morris, TT, Hansen, AF, Rasheed, H, Cho, Y, Chittoor, G, Ahlskog, R, Lind, PA, Palviainen, T, van der Zee, MD, Cheesman, R, Mangino, M, Wang, Y, Li, S, Klaric, L, Ratliff, SM, Bielak, LF, Nygaard, M, Giannelis, A, Willoughby, EA, Reynolds, CA, Balbona, JV, Andreassen, OA, Ask, H, Baras, A, Bauer, CR, Boomsma, DI, Campbell, A, Campbell, H, Chen, Z, Christofidou, P, Corfield, E, Dahm, CC, Dokuru, DR, Evans, LM, de Geus, EJC, Giddaluru, S, Gordon, SD, Harden, KP, Hill, WD, Hughes, A, Kerr, SM, Kim, Y, Kweon, H, Latvala, A, Lawlor, DA, Li, L, Lin, K, Magnus, P, Magnusson, PKE, Mallard, TT, Martikainen, P, Mills, MC, Njolstad, PR, Overton, JD, Pedersen, NL, Porteous, DJ, Reid, J, Silventoinen, K, Southey, MC, Stoltenberg, C, Tucker-Drob, EM, Wright, MJ, Hewitt, JK, Keller, MC, Stallings, MC, Lee, JJ, Christensen, K, Kardia, SLR, Peyser, PA, Smith, JA, Wilson, JF, Hopper, JL, Hagg, S, Spector, TD, Pingault, J-B, Plomin, R, Havdahl, A, Bartels, M, Martin, NG, Oskarsson, S, Justice, AE, Millwood, IY, Hveem, K, Naess, O, Willer, CJ, Asvold, BO, Koellinger, PD, Kaprio, J, Medland, SE, Walters, RG, Benjamin, DJ, Turley, P, Evans, DM, Smith, GD, Hayward, C, Brumpton, B, Hemani, G, Davies, NM, Howe, LJ, Nivard, MG, Morris, TT, Hansen, AF, Rasheed, H, Cho, Y, Chittoor, G, Ahlskog, R, Lind, PA, Palviainen, T, van der Zee, MD, Cheesman, R, Mangino, M, Wang, Y, Li, S, Klaric, L, Ratliff, SM, Bielak, LF, Nygaard, M, Giannelis, A, Willoughby, EA, Reynolds, CA, Balbona, JV, Andreassen, OA, Ask, H, Baras, A, Bauer, CR, Boomsma, DI, Campbell, A, Campbell, H, Chen, Z, Christofidou, P, Corfield, E, Dahm, CC, Dokuru, DR, Evans, LM, de Geus, EJC, Giddaluru, S, Gordon, SD, Harden, KP, Hill, WD, Hughes, A, Kerr, SM, Kim, Y, Kweon, H, Latvala, A, Lawlor, DA, Li, L, Lin, K, Magnus, P, Magnusson, PKE, Mallard, TT, Martikainen, P, Mills, MC, Njolstad, PR, Overton, JD, Pedersen, NL, Porteous, DJ, Reid, J, Silventoinen, K, Southey, MC, Stoltenberg, C, Tucker-Drob, EM, Wright, MJ, Hewitt, JK, Keller, MC, Stallings, MC, Lee, JJ, Christensen, K, Kardia, SLR, Peyser, PA, Smith, JA, Wilson, JF, Hopper, JL, Hagg, S, Spector, TD, Pingault, J-B, Plomin, R, Havdahl, A, Bartels, M, Martin, NG, Oskarsson, S, Justice, AE, Millwood, IY, Hveem, K, Naess, O, Willer, CJ, Asvold, BO, Koellinger, PD, Kaprio, J, Medland, SE, Walters, RG, Benjamin, DJ, Turley, P, Evans, DM, Smith, GD, Hayward, C, Brumpton, B, Hemani, G, and Davies, NM

Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Published
2022

2. Ability of known colorectal cancer susceptibility SNPs to predict colorectal cancer risk: A cohort study within the UK Biobank

Author

Koellinger, PD, Gafni, A, Dite, GS, Tuff, ES, Allman, R, Hopper, JL, Koellinger, PD, Gafni, A, Dite, GS, Tuff, ES, Allman, R, and Hopper, JL

Abstract

Colorectal cancer risk stratification is crucial to improve screening and risk-reducing recommendations, and consequently do better than a one-size-fits-all screening regimen. Current screening guidelines in the UK, USA and Australia focus solely on family history and age for risk prediction, even though the vast majority of the population do not have any family history. We investigated adding a polygenic risk score based on 45 single-nucleotide polymorphisms to a family history model (combined model) to quantify how it improves the stratification and discriminatory performance of 10-year risk and full lifetime risk using a prospective population-based cohort within the UK Biobank. For both 10-year and full lifetime risk, the combined model had a wider risk distribution compared with family history alone, resulting in improved risk stratification of nearly 2-fold between the top and bottom risk quintiles of the full lifetime risk model. Importantly, the combined model can identify people (n = 72,019) who do not have family history of colorectal cancer but have a predicted risk that is equivalent to having at least one affected first-degree relative (n = 44,950). We also confirmed previous findings by showing that the combined full lifetime risk model significantly improves discriminatory accuracy compared with a simple family history model 0.673 (95% CI 0.664-0.682) versus 0.666 (95% CI 0.657-0.675), p = 0.0065. Therefore, a combined polygenic risk score and first-degree family history model could be used to improve risk stratified population screening programs.

Published
2021

4. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits

Author

Grotzinger, AD, Rhemtulla, M, de Vlaming, R, Ritchie, S J, Mallard, TT, Hill, WD, Ip, HF, Marioni, RE, McIntosh, AM, Deary, IJ, Koellinger, PD, Harden, KP, Nivard, MG, Tucker-Drob, EM, Grotzinger, AD, Rhemtulla, M, de Vlaming, R, Ritchie, S J, Mallard, TT, Hill, WD, Ip, HF, Marioni, RE, McIntosh, AM, Deary, IJ, Koellinger, PD, Harden, KP, Nivard, MG, and Tucker-Drob, EM

Published
2019

5. An epigenome-wide association study meta-analysis of educational attainment

Author

Karlsson Linnér, R, Marioni, RE, Rietveld, CA, Simpkin, AJ, Davies, NM, Watanabe, K, Armstrong, NJ, Auro, K, Baumbach, C, Bonder, MJ, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, McRae, AF, Mandaviya, PR, Seppälä, I, Wang, Y, Baglietto, L, Binder, EB, Harris, SE, Hodge, AM, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, KA, Medland, SE, Metspalu, A, Milani, L, Milne, RL, Pattie, A, Pedersen, NL, Peters, A, Polidoro, S, Räikkönen, K, Severi, G, Starr, JM, Stolk, L, Waldenberger, M, BIOS Consortium, Eriksson, JG, Esko, T, Franke, L, Gieger, C, Giles, GG, Hägg, S, Jousilahti, P, Kaprio, J, Kähönen, M, Lehtimäki, T, Martin, NG, Van Meurs, JBC, Ollikainen, M, Perola, M, Posthuma, D, Raitakari, OT, Sachdev, PS, Taskesen, E, Uitterlinden, AG, Vineis, P, Wijmenga, C, Wright, MJ, Relton, C, Davey Smith, G, Deary, IJ, Koellinger, PD, and Benjamin, DJ

Subjects
17 Psychology And Cognitive Sciences, Psychiatry, BIOS Consortium, 11 Medical And Health Sciences, 06 Biological Sciences
Abstract

The epigenome is associated with biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here we provide evidence on the associations between epigenetic modifications-in our case, CpG methylation-and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10 767 individuals. We find nine CpG probes significantly associated with EA. However, robustness analyses show that all nine probes have previously been found to be associated with smoking. Only two associations remain when we perform a sensitivity analysis in the subset of never-smokers, and these two probes are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, the effect sizes of the associations with EA are far smaller than the known associations with the biologically proximal environmental factors alcohol consumption, body mass index, smoking and maternal smoking during pregnancy. Follow-up analyses that combine the effects of many probes also point to small methylation associations with EA that are highly correlated with the combined effects of smoking. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.Molecular Psychiatry advance online publication, 31 October 2017; doi:10.1038/mp.2017.210.

Published
2017

6. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

Author

Lee, JJ, Wedow, R, Okbay, A, Kong, E, Maghzian, O, Zacher, M, Tuan Anh, N-V, Bowers, P, Sidorenko, J, Linner, RK, Fontana, MA, Kundu, T, Lee, C, Li, H, Li, R, Royer, R, Timshel, PN, Walters, RK, Willoughby, EA, Yengo, L, Alver, M, Bao, Y, Clark, DW, Day, FR, Furlotte, NA, Joshi, PK, Kemper, KE, Kleinman, A, Langenberg, C, Magi, R, Trampush, JW, Verma, SS, Wu, Y, Lam, Mei, Zhao, JH, Zheng, Z, Boardman, JD, Campbell, H, Freese, J, Harris, KM, Hayward, C, Herd, P, Kumari, M, Lencz, T, Luan, JA, Malhotra, AK, Metspalu, A, Milani, L, Ong, KK, Perry, JRB, Porteous, DJ, Ritchie, MD, Smart, MC, Smith, BH, Tung, JY, Wareham, NJ, Wilson, JF, Beauchamp, JP, Conley, DC, Esko, T, Lehrer, SF, Magnusson, PKE, Oskarsson, S, Pers, TH, Robinson, MR, Thom, K, Watson, C, Chabris, CF, Meyer, MN, Laibson, DI, Yang, Jiaqi, Johannesson, M, Koellinger, PD, Turley, P, Visscher, PM, Benjamin, DJ, Cesarini, D, Lee, JJ, Wedow, R, Okbay, A, Kong, E, Maghzian, O, Zacher, M, Tuan Anh, N-V, Bowers, P, Sidorenko, J, Linner, RK, Fontana, MA, Kundu, T, Lee, C, Li, H, Li, R, Royer, R, Timshel, PN, Walters, RK, Willoughby, EA, Yengo, L, Alver, M, Bao, Y, Clark, DW, Day, FR, Furlotte, NA, Joshi, PK, Kemper, KE, Kleinman, A, Langenberg, C, Magi, R, Trampush, JW, Verma, SS, Wu, Y, Lam, Mei, Zhao, JH, Zheng, Z, Boardman, JD, Campbell, H, Freese, J, Harris, KM, Hayward, C, Herd, P, Kumari, M, Lencz, T, Luan, JA, Malhotra, AK, Metspalu, A, Milani, L, Ong, KK, Perry, JRB, Porteous, DJ, Ritchie, MD, Smart, MC, Smith, BH, Tung, JY, Wareham, NJ, Wilson, JF, Beauchamp, JP, Conley, DC, Esko, T, Lehrer, SF, Magnusson, PKE, Oskarsson, S, Pers, TH, Robinson, MR, Thom, K, Watson, C, Chabris, CF, Meyer, MN, Laibson, DI, Yang, Jiaqi, Johannesson, M, Koellinger, PD, Turley, P, Visscher, PM, Benjamin, DJ, and Cesarini, D

Published
2018

9. An epigenome-wide association study meta-analysis of educational attainment

Author

Linner, RK, Marioni, RE, Rietveld, Niels, Simpkin, AJ, Davies, NM, Watanabe, K, Armstrong, NJ, Auro, K, Baumbach, C, Bonder, MJ, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, Mcrae, AF, Mandaviya, Pooja, Seppala, I, Wang, Y, Baglietto, L, Binder, EB, Harris, SE, Hodge, AM, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, KA, Medland, SE, Metspalu, A, Milani, L, Milne, RL, Pattie, A, Pedersen, NL, Peters, A, Polidoro, S, Raikkonen, K, Severi, G, Starr, JM, Stolk, Lisette, Waldenberger, M, Eriksson, JG, Esko, T, Franke, L, Gieger, C, Giles, GG, Hagg, S, Jousilahti, P, Kaprio, J, Kahonen, M, Lehtimaki, T, Martin, NG, van Meurs, Joyce, Ollikainen, M, Perola, M, Posthuma, D, Raitakari, OT, Sachdev, PS, Taskesen, E, Uitterlinden, André, Vineis, P, Wijmenga, C, Wright, MJ, Relton, C, Smith, GD, Deary, IJ, Koellinger, PD, Benjamin, DJ, Linner, RK, Marioni, RE, Rietveld, Niels, Simpkin, AJ, Davies, NM, Watanabe, K, Armstrong, NJ, Auro, K, Baumbach, C, Bonder, MJ, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, Mcrae, AF, Mandaviya, Pooja, Seppala, I, Wang, Y, Baglietto, L, Binder, EB, Harris, SE, Hodge, AM, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, KA, Medland, SE, Metspalu, A, Milani, L, Milne, RL, Pattie, A, Pedersen, NL, Peters, A, Polidoro, S, Raikkonen, K, Severi, G, Starr, JM, Stolk, Lisette, Waldenberger, M, Eriksson, JG, Esko, T, Franke, L, Gieger, C, Giles, GG, Hagg, S, Jousilahti, P, Kaprio, J, Kahonen, M, Lehtimaki, T, Martin, NG, van Meurs, Joyce, Ollikainen, M, Perola, M, Posthuma, D, Raitakari, OT, Sachdev, PS, Taskesen, E, Uitterlinden, André, Vineis, P, Wijmenga, C, Wright, MJ, Relton, C, Smith, GD, Deary, IJ, Koellinger, PD, and Benjamin, DJ

Abstract

The epigenome is associated with biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here we provide evidence on the associations between epigenetic modifications—in our case, CpG methylation—and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10?767 individuals. We find nine CpG probes significantly associated with EA. However, robustness analyses show that all nine probes have previously been found to be associated with smoking. Only two associations remain when we perform a sensitivity analysis in the subset of never-smokers, and these two probes are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, the effect sizes of the associations with EA are far smaller than the known associations with the biologically proximal environmental factors alcohol consumption, body mass index, smoking and maternal smoking during pregnancy. Follow-up analyses that combine the effects of many probes also point to small methylation associations with EA that are highly correlated with the combined effects of smoking. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.

Published
2017

10. Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies

Author

Vlaming, Ronald, Okbay, Aysu, Rietveld, Niels, Johannesson, M, Magnusson, PKE, Uitterlinden, André, van Rooij, FJA, Hofman, Bert, Groenen, Patrick, Thurik, Roy, Koellinger, PD, Vlaming, Ronald, Okbay, Aysu, Rietveld, Niels, Johannesson, M, Magnusson, PKE, Uitterlinden, André, van Rooij, FJA, Hofman, Bert, Groenen, Patrick, Thurik, Roy, and Koellinger, PD

Abstract

Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called ‘missing heritability’. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51–62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36–38%). Hence, cross-study heterogeneity contributes to the missing heritability.

Published
2017

11. Common genetic variants associated with cognitive performance identified using the proxy-phenotype method (vol 111, pg 13790, 2014)

Author

Rietveld, CA, Esko, T, Davies, G, Pers, TH, Turley, P, Benyamin, B, Chabris, CF, Emilsson, V, Johnson, AD, Lee, JJ, de Leeuw, C, Marioni, RE, Medland, SE, Miller, MB, Rostapshova, O, van der Lee, Sven, Vinkhuyzen, AAE, Amin, Najaf, Conley, D, Derringer, J, Duijn, Cornelia, Fehrmann, R, Franke, L, Glaeser, EL, Hansell, NK, Hayward, C, Iacono, WG, Ibrahim-Verbaas, C, Jaddoe, Vincent, Karjalainen, J, Laibson, D, Lichtenstein, P, Liewald, DC, Magnusson, PKE, Martin, NG, McGue, M, McMahon, G, Pedersen, NL, Pinker, S, Porteous, DJ, Posthuma, Daniëlle, Rivadeneira, Fernando, Smith, BH, Starr, JM, Tiemeier, Henning, Timpson, NJ, Trzaskowski, M, Uitterlinden, André, Verhulst, Frank, Ward, ME, Wright, MJ, Smith, GD, Deary, IJ, Johannesson, M, Plomin, R, Visscher, P, Benjamin, DJ, Cesarini, D, Koellinger, PD, Epidemiology, Erasmus MC other, Child and Adolescent Psychiatry / Psychology, Internal Medicine, and Psychiatry

Published
2015

12. Replicability and Robustness of GWAS for Behavioral Traits

Author

Rietveld, Niels, Conley, D, Eriksson, N, Esko, T, Medland, SE, Vinkhuyzen, AAE, Yang, Jiaqi, Boardman, JD, Chabris, CF, Dawes, CT, Domingue, BW, Hinds, DA, Johannesson, M, Kiefer, AK, Laibson, D, Magnusson, PKE, Mountain, JL, Oskarsson, S, Rostapshova, O, Teumer, A, Tung, JY, Visscher, PM, Benjamin, DJ, Cesarini, D, Koellinger, PD, and Applied Economics

Subjects
Principal Component Analysis, Genotype, Massachusetts, Educational Status, Humans, Reproducibility of Results, Queensland, Registries, Achievement, Polymorphism, Single Nucleotide, Article, Genome-Wide Association Study
Abstract

A recent genome-wide-association study of educational attainment identified three single-nucleotide polymorphisms (SNPs) whose associations, despite their small effect sizes (each R (2) ? 0.02%), reached genome-wide significance (p < 5 × 10(-8)) in a large discovery sample and were replicated in an independent sample (p < .05). The study also reported associations between educational attainment and indices of SNPs called "polygenic scores." In three studies, we evaluated the robustness of these findings. Study 1 showed that the associations with all three SNPs were replicated in another large (N = 34,428) independent sample. We also found that the scores remained predictive (R (2) ? 2%) in regressions with stringent controls for stratification (Study 2) and in new within-family analyses (Study 3). Our results show that large and therefore well-powered genome-wide-association studies can identify replicable genetic associations with behavioral traits. The small effect sizes of individual SNPs are likely to be a major contributing factor explaining the striking contrast between our results and the disappointing replication record of most candidate-gene studies.

Published
2014

13. Genome-wide association study identifies 74 loci associated with educational attainment

Author

Okbay, Aysu, Beauchamp, JP, Fontana, MA, Lee, JJ, Pers, TH, Rietveld, Niels, Turley, P, Chen, GB, Emilsson, V, Meddens, SFW, Oskarsson, S, Pickrell, JK, Thom, K, Timshel, P, Vlaming, Ronald, Abdellaoui, A, Ahluwalia, TS, Bacelis, J, Baumbach, C, Bjornsdottir, G, Brandsma, Johan, Concas, MP, Derringer, J, Furlotte, NA, Galesloot, TE, Girotto, G, Gupta, R, Hall, LM, Harris, SE, Hofer, E, Horikoshi, M, Huffman, JE, Kaasik, K, Kalafati, IP, Karlsson, R, Kong, A, Lahti, J, van der Lee, Sven, de Leeuw, C, Lind, PA, Lindgren, KO, Liu, T, Mangino, M, Marten, J, Mihailov, E, Miller, MB, van der Most, PJ, Oldmeadow, C, Payton, A, Pervjakova, N, Peyrot, WJ, Qian, Y, Raitakari, O, Rueedi, R, Salvi, E, Schmidt, B, Schraut, KE, Shi, JX, Smith, AV, Poot, Raymond, St Pourcain, B, Teumer, A, Thorleifsson, G, Verweij, N (Niek), Vuckovic, D, Wellmann, J, Westra, HJ, Yang, JY, Zhao, W, Zhu, ZH, Alizadeh, BZ, Amin, Najaf, Bakshi, A, Baumeister, SE, Biino, G, Bonnelykke, K, Boyle, PA, Campbell, H, Cappuccio, FP, Davies, G, De Neve, JE, Deloukas, P, Demuth, I, Ding, J, Eibich, P, Eisele, L, Eklund, N, Evans, DM, Faul, JD, Feitosa, MF, Forstner, AJ, Gandin, I, Gunnarsson, B, Halldorsson, BV, Harris, TB, Heath, AC, Hocking, LJ, Holliday, EG, Homuth, G, Horan, MA, Hottenga, JJ, De Jager, PL, Joshi, PK, Jugessur, A, Kaakinen, MA, Kahonen, M, Kanoni, S, Keltigangas-Jarvinen, L, Kiemeney, LALM, Kolcic, I, Koskinen, S, Kraja, AT, Kroh, M, Kutalik, Z, Latvala, A, Launer, LJ, Lebreton, MP, Levinson, DF, Lichtenstein, P, Lichtner, P, Liewald, DCM, Loukola, A, Madden, PA, Magi, R, Maki-Opas, T, Marioni, RE, Marques-Vidal, P, Meddens, GA, McMahon, G, Meisinger, C, Meitinger, T, Milaneschi, Y, Milani, L, Montgomery, GW, Myhre, R, Nelson, CP, Nyholt, DR, Ollier, WER, Palotie, A, Paternoster, L, Pedersen, NL, Petrovic, KE, Porteous, DJ, Raikkonen, K, Ring, SM, Robino, A, Rostapshova, O, Rudan, I, Rustichini, A, Salomaa, V, Sanders, AR, Sarin, AP, Schmidt, Heléna, Scott, RJ, Smith, BH, Smith, JA, Staessen, JA, Steinhagen-Thiessen, E, Strauch, K, Terracciano, A, Tobin, MD, Ulivi, S, Vaccargiu, S, Quaye, L, van Rooij, FJA, Venturini, C, Vinkhuyzen, AAE, Volker, U, Volzke, H, Vonk, JM, Vozzi, D, Waage, J, Ware, EB, Willemsen, G, Attia, JR, Bennett, DA, Berger, K, Bertram, L, Bisgaard, H, Boomsma, DI, Borecki, IB, Bultmann, U, Chabris, CF, Cucca, F, Cusi, D, Deary, IJ, Dedoussis, GV, Duijn, Cornelia, Eriksson, JG, Franke, B, Franke, L, Gasparini, P, Gejman, PV, Gieger, C, Grabe, HJ, Gratten, J, Groenen, Patrick, Gudnason, V, van der Harst, P, Hayward, C, Hinds, DA, Hoffmann, W, Hyppnen, E, Iacono, WG, Jacobsson, B, Jarvelin, MR, Jockel, KH, Kaprio, J, Kardia, SLR, Lehtimaki, T, Lehrer, SF, Magnusson, PKE, Martin, NG, McGue, M, Metspalu, A, Pendleton, N, Penninx, BWJH, Perola, M, Pirastu, N, Pirastu, M, Polasek, O, Posthuma, Daniëlle, Power, C, Province, MA, Samani, NJ, Schlessinger, D, Schmidt, R, Sorensen, TIA, Spector, TD, Stefansson, K, Thorsteinsdottir, U, Thurik, Roy, Timpson, NJ, Tiemeier, Henning, Tung, JY, Uitterlinden, André, Vitart, V, Vollenweider, P, Weir, DR, Wilson, JF, Wright, AF, Conley, DC, Krueger, RF, Smith, GD, Hofman, Bert, Laibson, DI, Medland, SE, Meyer, MN, Yang, Jiaqi, Johannesson, M, Visscher, PM, Esko, T, Koellinger, PD, Cesarini, D, Benjamin, DJ, Okbay, Aysu, Beauchamp, JP, Fontana, MA, Lee, JJ, Pers, TH, Rietveld, Niels, Turley, P, Chen, GB, Emilsson, V, Meddens, SFW, Oskarsson, S, Pickrell, JK, Thom, K, Timshel, P, Vlaming, Ronald, Abdellaoui, A, Ahluwalia, TS, Bacelis, J, Baumbach, C, Bjornsdottir, G, Brandsma, Johan, Concas, MP, Derringer, J, Furlotte, NA, Galesloot, TE, Girotto, G, Gupta, R, Hall, LM, Harris, SE, Hofer, E, Horikoshi, M, Huffman, JE, Kaasik, K, Kalafati, IP, Karlsson, R, Kong, A, Lahti, J, van der Lee, Sven, de Leeuw, C, Lind, PA, Lindgren, KO, Liu, T, Mangino, M, Marten, J, Mihailov, E, Miller, MB, van der Most, PJ, Oldmeadow, C, Payton, A, Pervjakova, N, Peyrot, WJ, Qian, Y, Raitakari, O, Rueedi, R, Salvi, E, Schmidt, B, Schraut, KE, Shi, JX, Smith, AV, Poot, Raymond, St Pourcain, B, Teumer, A, Thorleifsson, G, Verweij, N (Niek), Vuckovic, D, Wellmann, J, Westra, HJ, Yang, JY, Zhao, W, Zhu, ZH, Alizadeh, BZ, Amin, Najaf, Bakshi, A, Baumeister, SE, Biino, G, Bonnelykke, K, Boyle, PA, Campbell, H, Cappuccio, FP, Davies, G, De Neve, JE, Deloukas, P, Demuth, I, Ding, J, Eibich, P, Eisele, L, Eklund, N, Evans, DM, Faul, JD, Feitosa, MF, Forstner, AJ, Gandin, I, Gunnarsson, B, Halldorsson, BV, Harris, TB, Heath, AC, Hocking, LJ, Holliday, EG, Homuth, G, Horan, MA, Hottenga, JJ, De Jager, PL, Joshi, PK, Jugessur, A, Kaakinen, MA, Kahonen, M, Kanoni, S, Keltigangas-Jarvinen, L, Kiemeney, LALM, Kolcic, I, Koskinen, S, Kraja, AT, Kroh, M, Kutalik, Z, Latvala, A, Launer, LJ, Lebreton, MP, Levinson, DF, Lichtenstein, P, Lichtner, P, Liewald, DCM, Loukola, A, Madden, PA, Magi, R, Maki-Opas, T, Marioni, RE, Marques-Vidal, P, Meddens, GA, McMahon, G, Meisinger, C, Meitinger, T, Milaneschi, Y, Milani, L, Montgomery, GW, Myhre, R, Nelson, CP, Nyholt, DR, Ollier, WER, Palotie, A, Paternoster, L, Pedersen, NL, Petrovic, KE, Porteous, DJ, Raikkonen, K, Ring, SM, Robino, A, Rostapshova, O, Rudan, I, Rustichini, A, Salomaa, V, Sanders, AR, Sarin, AP, Schmidt, Heléna, Scott, RJ, Smith, BH, Smith, JA, Staessen, JA, Steinhagen-Thiessen, E, Strauch, K, Terracciano, A, Tobin, MD, Ulivi, S, Vaccargiu, S, Quaye, L, van Rooij, FJA, Venturini, C, Vinkhuyzen, AAE, Volker, U, Volzke, H, Vonk, JM, Vozzi, D, Waage, J, Ware, EB, Willemsen, G, Attia, JR, Bennett, DA, Berger, K, Bertram, L, Bisgaard, H, Boomsma, DI, Borecki, IB, Bultmann, U, Chabris, CF, Cucca, F, Cusi, D, Deary, IJ, Dedoussis, GV, Duijn, Cornelia, Eriksson, JG, Franke, B, Franke, L, Gasparini, P, Gejman, PV, Gieger, C, Grabe, HJ, Gratten, J, Groenen, Patrick, Gudnason, V, van der Harst, P, Hayward, C, Hinds, DA, Hoffmann, W, Hyppnen, E, Iacono, WG, Jacobsson, B, Jarvelin, MR, Jockel, KH, Kaprio, J, Kardia, SLR, Lehtimaki, T, Lehrer, SF, Magnusson, PKE, Martin, NG, McGue, M, Metspalu, A, Pendleton, N, Penninx, BWJH, Perola, M, Pirastu, N, Pirastu, M, Polasek, O, Posthuma, Daniëlle, Power, C, Province, MA, Samani, NJ, Schlessinger, D, Schmidt, R, Sorensen, TIA, Spector, TD, Stefansson, K, Thorsteinsdottir, U, Thurik, Roy, Timpson, NJ, Tiemeier, Henning, Tung, JY, Uitterlinden, André, Vitart, V, Vollenweider, P, Weir, DR, Wilson, JF, Wright, AF, Conley, DC, Krueger, RF, Smith, GD, Hofman, Bert, Laibson, DI, Medland, SE, Meyer, MN, Yang, Jiaqi, Johannesson, M, Visscher, PM, Esko, T, Koellinger, PD, Cesarini, D, and Benjamin, DJ

Abstract

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals(1). Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample(1,2) of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

Published
2016

14. Genetic variation associated with differential educational attainment in adults has anticipated associations with school performance in children

Author

Ward, ME, McMahon, G, St Pourcain, B, Evans, DM, Rietveld, Niels, Benjamin, DJ, Koellinger, PD, Cesarini, D, SSGAC, The, Davey Smith, G, Timpson, NJ, and Applied Economics

Subjects
lcsh:R, lcsh:Medicine, lcsh:Q, lcsh:Science
Abstract

Genome-wide association study results have yielded evidence for the association of common genetic variants with crude measures of completed educational attainment in adults. Whilst informative, these results do not inform as to the mechanism of these effects or their presence at earlier ages and where educational performance is more routinely and more precisely assessed. Single nucleotide polymorphisms exhibiting genome-wide significant associations with adult educational attainment were combined to derive an unweighted allele score in 5,979 and 6,145 young participants from the Avon Longitudinal Study of Parents and Children with key stage 3 national curriculum test results (SATS results) available at age 13 to 14 years in English and mathematics respectively. Standardised (z-scored) results for English and mathematics showed an expected relationship with sex, with girls exhibiting an advantage over boys in English (0.433 SD (95%CI 0.395, 0.470), p

Published
2014

15. The Molecular Genetic Architecture of Self-Employment

Author

van der Loos MJ, Rietveld CA, Eklund N, Koellinger PD, Rivadeneira F, Abecasis GR, Ankra-Badu GA, Baumeister SE, Benjamin DJ, Biffar R, Blankenberg S, Boomsma DI, Cesarini D, Cucca F, de Geus EJ, Hottenga JJ, Surakka I, Svento R, Terracciano A, Tikkanen E, van Duijn CM, Viikari J, Vxf6lzke H, Wichmann HE, Wild PS, Willems SM, Willemsen G, van Rooij FJ, Groenen PJ, Uitterlinden AG, Hofman A, and Thurik AR

Published
2013

16. The heritability of moral standards for everyday dishonesty

Author

Mazar, M, Dawes, C, Loewen, PJ, Cesarini, D, Koellinger, PD, Johannesson, M, Magnusson, PKE, Applied Economics, ABS Other Research (FEB), Economics, Tinbergen Institute, and Amsterdam Neuroscience - Complex Trait Genetics

Subjects
Ethics, Organizational Behavior and Human Resource Management, Economics and Econometrics, Dishonesty, media_common.quotation_subject, education, Moral standards, Sample (statistics), Variation, SDG 10 - Reduced Inequalities, Heritability, Variation (linguistics), Genetic, Perception, Genetic variation, Individual differences, Heterogeneity, Psychology, Social psychology, psychological phenomena and processes, media_common
Abstract

Previous research on the acceptability of dishonest actions has focused on the role of social norms and internal reward mechanisms. Using a sample of over 2000 Swedish adult twins, this manuscript examines whether there exists another source that is driving differences in perceptions of the acceptability of dishonest actions: genetic variation. We find that much of the variation in perceptions of the acceptability of dishonest actions is attributable to genetic variation between individuals.

Published
2013
Full Text
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18. Gender differences in entrepreneurial propensity

Author

Koellinger, PD, Minniti, M, Schade, Ch, ABS Other Research (FEB), Economics, Tinbergen Institute, Amsterdam Neuroscience - Complex Trait Genetics, and Applied Economics

Subjects
SDG 5 - Gender Equality, SDG 8 - Decent Work and Economic Growth
Abstract

Using data from representative population surveys in 17 countries, we find that the lower rate of female business ownership is primarily due to women's lower propensity to start businesses rather than to differences in survival rates across genders. We show that women are less confident in their entrepreneurial skills, have different social networks and exhibit higher fear of failure than men. After controlling for endogeneity, we find that these variables explain a substantial part of the gender gap in entrepreneurial activity. Although, of course, their relative importance varies significantly across countries, these factors appear to have a universal effect.

Published
2013
Full Text
View/download PDF

20. The molecular genetic architecture of economic and political preferences

Author

Benjamin, DJ, Cesarini, D, van der Loos, MJHM (Matthijs), Dawes, CT, Koellinger, PD, Magnusson, PKE, Chabris, CF, Conley, D, Laibson, DI, Johannesson, M, Visscher, PM, Erasmus School of Economics, and Applied Economics

Abstract

Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic–based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.

Published
2012

22. Protecting against low probability desasters - A large stake experiment

Author

Schade, Ch, Kunreuther, HC, Koellinger, PD, and Applied Economics

Abstract

We carry out a large monetary stakes insurance experiment with very small probabilities of losses and ambiguous as well as exact probabilities. Many individuals do not want to pay anything for insurance whether the probabilities are given exactly or are ambiguous. Many others, however, are willing to pay surprisingly large amounts. With ambiguity, the percentage of those paying nothing is smaller and the willingness to pay (WTP) of the other individuals larger than with exact probabilities. Comparing elasticities with ambiguity, we find that worry is much more important than subjective probability in determining WTP for insurance. Furthermore, when the ambiguous loss probability is increased by a factor of 1000, it has almost no effect on WTP.

Published
2011

24. De genetica van ondernemerschap

Author

van der Loos, MJHM (Matthijs), Groenen, Patrick, Hofman, Bert, Koellinger, PD, Rivadeneira, Fernando, van Rooij, FJA, Thurik, Roy, Uitterlinden, André, Erasmus School of Economics, Econometrics, Epidemiology, Applied Economics, and Internal Medicine

Published
2011

26. A genome-wide association study of educational attainment

Author

Beauchamp, JP, Cesarini, D, van der Loos, MJHM (Matthijs), Koellinger, PD, Groenen, Patrick, Fowler, JH, Rosenquist, JN, Thurik, Roy, Christakis, NA, Erasmus School of Economics, Applied Economics, and Econometrics

Published
2010

29. The Molecular Genetic Architecture of Self-Employment

Author

van der Loos, MJHM, Rietveld, CA, Eklund, N, Koellinger, PD, Rivadeneira, F, Abecasis, GR, Ankra-Badu, GA, Baumeister, SE, Benjamin, DJ, Biffar, R, Blankenberg, S, Boomsma, DI, Cesarini, D, Cucca, F, de Geus, EJC, Dedoussis, G, Deloukas, P, Dimitriou, M, Eiriksdottir, G, Eriksson, J, Gieger, C, Gudnason, V, Höhne, B, Holle, R, Hottenga, JJ, Isaacs, A, Järvelin, MR, Johannesson, M, Kaakinen, M, Kähönen, M, Kanoni, S, Laaksonen, MA, Lahti, J, Launer, LJ, Lehtimäki, T, Loitfelder, M, Magnusson, PKE, Naitza, S, Oostra, BA, Perola, M, Petrovic, K, Quaye, L, Raitakari, O, Ripatti, S, Scheet, P, Schlessinger, D, Schmidt, CO, Schmidt, H, Schmidt, R, Senft, A, Smith, AV, Spector, TD, Surakka, I, Svento, R, Terracciano, A, Tikkanen, E, van Duijn, CM, Viikari, J, Völzke, H, Wichmann, HE, Wild, PS, Willems, SM, Willemsen, G, van Rooij, FJA, Groenen, PJF, Uitterlinden, AG, Hofman, A, Thurik, AR, van der Loos, MJHM, Rietveld, CA, Eklund, N, Koellinger, PD, Rivadeneira, F, Abecasis, GR, Ankra-Badu, GA, Baumeister, SE, Benjamin, DJ, Biffar, R, Blankenberg, S, Boomsma, DI, Cesarini, D, Cucca, F, de Geus, EJC, Dedoussis, G, Deloukas, P, Dimitriou, M, Eiriksdottir, G, Eriksson, J, Gieger, C, Gudnason, V, Höhne, B, Holle, R, Hottenga, JJ, Isaacs, A, Järvelin, MR, Johannesson, M, Kaakinen, M, Kähönen, M, Kanoni, S, Laaksonen, MA, Lahti, J, Launer, LJ, Lehtimäki, T, Loitfelder, M, Magnusson, PKE, Naitza, S, Oostra, BA, Perola, M, Petrovic, K, Quaye, L, Raitakari, O, Ripatti, S, Scheet, P, Schlessinger, D, Schmidt, CO, Schmidt, H, Schmidt, R, Senft, A, Smith, AV, Spector, TD, Surakka, I, Svento, R, Terracciano, A, Tikkanen, E, van Duijn, CM, Viikari, J, Völzke, H, Wichmann, HE, Wild, PS, Willems, SM, Willemsen, G, van Rooij, FJA, Groenen, PJF, Uitterlinden, AG, Hofman, A, and Thurik, AR

Abstract

Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable-entrepreneurship-that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σg2/σP2 = 25%, h2 = 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10-5 were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases. © 2013 van der Loos et al.

Published
2013

30. The Molecular Genetic Architecture of Self-Employment

Author

van der Loos, MJHM (Matthijs), Rietveld, Niels, Eklund, N, Koellinger, PD, Rivadeneira, Fernando, Abecasis, GR, Ankra-Badu, GA, Baumeister, SE, Benjamin, DJ, Biffar, R, Blankenberg, S, Boomsma, DI, Cesarini, D, Cucca, F, de Geus, EJC, Dedoussis, G, Deloukas, P, Dimitriou, M, Eiriksdottir, G, Eriksson, J, Gieger, C, Gudnason, V, Hoehne, B, Holle, R, Hottenga, JJ (Jouke Jan), Isaacs, Aaron, Jarvelin, MR, Johannesson, M, Kaakinen, M, Kahonen, M, Kanoni, S, Laaksonen, MA, Lahti, J, Launer, LJ (Lenore), Lehtimaki, T, Loitfelder, M, Magnusson, PKE, Naitza, S, Oostra, Ben, Perola, M, Petrovic, K, Quaye, L, Raitakari, O, Ripatti, S, Scheet, P, Schlessinger, D, Schmidt, CO, Schmidt, H, Schmidt, R, Senft, A, Smith, AV, Spector, TD, Surakka, I, Svento, R, Terracciano, A, Tikkanen, E, van Duijn, CM, Viikari, J, Voelzke, Henry, Wichmann, H-E, Wild, PS, Willems, SM, Willemsen, G, van Rooij, FJA, Groenen, Patrick, Uitterlinden, André, Hofman, Bert, Thurik, Roy, van der Loos, MJHM (Matthijs), Rietveld, Niels, Eklund, N, Koellinger, PD, Rivadeneira, Fernando, Abecasis, GR, Ankra-Badu, GA, Baumeister, SE, Benjamin, DJ, Biffar, R, Blankenberg, S, Boomsma, DI, Cesarini, D, Cucca, F, de Geus, EJC, Dedoussis, G, Deloukas, P, Dimitriou, M, Eiriksdottir, G, Eriksson, J, Gieger, C, Gudnason, V, Hoehne, B, Holle, R, Hottenga, JJ (Jouke Jan), Isaacs, Aaron, Jarvelin, MR, Johannesson, M, Kaakinen, M, Kahonen, M, Kanoni, S, Laaksonen, MA, Lahti, J, Launer, LJ (Lenore), Lehtimaki, T, Loitfelder, M, Magnusson, PKE, Naitza, S, Oostra, Ben, Perola, M, Petrovic, K, Quaye, L, Raitakari, O, Ripatti, S, Scheet, P, Schlessinger, D, Schmidt, CO, Schmidt, H, Schmidt, R, Senft, A, Smith, AV, Spector, TD, Surakka, I, Svento, R, Terracciano, A, Tikkanen, E, van Duijn, CM, Viikari, J, Voelzke, Henry, Wichmann, H-E, Wild, PS, Willems, SM, Willemsen, G, van Rooij, FJA, Groenen, Patrick, Uitterlinden, André, Hofman, Bert, and Thurik, Roy

Published
2013

31. Candidate gene studies and the quest for the entrepreneurial gene

Author

van der Loos, MJHM (Matthijs), Koellinger, PD, Groenen, Patrick, Rietveld, Niels, Rivadeneira, Fernando, van Rooij, FJA, Uitterlinden, André, Hofman, Bert, Thurik, Roy, van der Loos, MJHM (Matthijs), Koellinger, PD, Groenen, Patrick, Rietveld, Niels, Rivadeneira, Fernando, van Rooij, FJA, Uitterlinden, André, Hofman, Bert, and Thurik, Roy

Abstract

Candidate gene studies of human behavior are gaining interest in economics and entrepreneurship research. Performing and interpreting these studies is not straightforward because the selection of candidates influences the interpretation of the results. As an example, Nicolaou et al. (Small Bus Econ 36:151¿155, 2011) report a significant association between a common genetic variant in the DRD3 gene and the tendency to be an entrepreneur. We fail to replicate this finding using a much larger, independent dataset. In addition, we discuss the candidate gene approach and give suggestions to avoid the publication of false positives.

Published
2011

32. Gender differences in entrepreneurial propensity

Author

Koellinger, PD, Minniti, M, Schade, Ch, Koellinger, PD, Minniti, M, and Schade, Ch

Abstract

Using data from representative population surveys in 17 countries, we find that the lower rate of female business ownership is primarily due to women's lower propensity to start businesses rather than to differences in survival rates across genders. We show that women are less confident in their entrepreneurial skills, have different social networks and exhibit higher fear of failure than men. After controlling for endogeneity, we find that these variables explain a substantial part of the gender gap in entrepreneurial activity. Although, of course, their relative importance varies significantly across countries, these factors appear to have a universal effect.

Published
2011

34. Linear Mixed Models in Statistical Genetics

Author

Vlaming, Ronald, Thurik, Roy, Groenen, Patrick, Koellinger, PD, and Applied Economics

Abstract

One of the goals of statistical genetics is to elucidate the genetic architecture of phenotypes (i.e., observable individual characteristics) that are affected by many genetic variants (e.g., single-nucleotide polymorphisms; SNPs). A particular aim is to identify specific SNPs that are robustly associated with a given phenotype using a so-called genome-wide association study (GWAS). Although GWAS sample sizes have increased in recent years, the number of SNPs still tends to vastly exceed sample sizes. Hence, multiple regression cannot be used to infer the association between SNPs and a phenotype jointly. Instead, the linear mixed model (LMM) has become a popular tool in statistical genetics. By placing a reasonable prior on SNP effects, LMMs can be used to jointly estimate SNP effects and to infer their contribution to phenotypic variance. In this dissertation, I investigate several aspects of LMMs and related methods, such as ridge regression and LD-score regression. In addition, an LMM is used to develop an online tool, called MetaGAP, which quantifies the statistical power of a GWAS in case of heterogeneity in underlying subsamples. Using MetaGAP, I show that ongoing GWAS efforts are well-powered even for considerably heterogeneous phenotypes. This prediction is bolstered by a GWAS of reproductive choices, reported here, that finds twelve robustly associated SNPs. I conclude that current GWAS sample sizes enable researchers to uncover parts of the genetic architecture of complex social-scientific outcomes and posit that GWAS efforts will soon attain sufficient predictive accuracy for useful applications throughout the social sciences.

Published
2017

36. Genetic variants linked to education predict longevity

Author

Chris Power, Gail Davies, Ilaria Gandin, Panagiotis Deloukas, Jennifer E. Huffman, Pascal Timshel, Albert V. Smith, A. Kong, Paul Lichtenstein, Joseph K. Pickrell, Philipp Koellinger, P. L. De Jager, Reedik Mägi, G. B. Chen, Neil Pendleton, B. V. Halldórsson, George Dedoussis, Antti-Pekka Sarin, Natalia Pervjakova, Veikko Salomaa, Simona Vaccargiu, Ozren Polasek, K. H. Jöckel, Elisabeth Steinhagen-Thiessen, Y. Milaneschi, Jessica D. Faul, Patricia A. Boyle, Patrik K. E. Magnusson, Igor Rudan, Christopher P. Nelson, Vilmundur Gudnason, John Attia, Jürgen Wellmann, Kristi Läll, Konstantin Strauch, Stuart J. Ritchie, Markus Perola, Nicola Pirastu, Klaus Bønnelykke, Robert Karlsson, R. de Vlaming, Liisa Keltigangas-Jarvinen, Thomas Meitinger, Riccardo E. Marioni, Anu Loukola, Barbera Franke, Reinhold Schmidt, Maël Lebreton, Sven Oskarsson, E. Mihailov, Harm-Jan Westra, David R. Weir, Aldi T. Kraja, Niek Verweij, Peter M. Visscher, Hans-Jörgen Grabe, Johannes H. Brandsma, Mark Adams, R. J. Scott, G. Thorleifsson, Tõnu Esko, Mika Kähönen, Saskia P. Hagenaars, Patrick Turley, Johannes Waage, Peter Lichtner, Dragana Vuckovic, Antonietta Robino, Henry Völzke, Lydia Quaye, C. de Leeuw, Marika Kaakinen, Wei Zhao, Abdel Abdellaoui, Reka Nagy, Pedro Marques-Vidal, Johan G. Eriksson, Alan F. Wright, Andres Metspalu, Lavinia Paternoster, Momoko Horikoshi, Jan A. Staessen, Tarunveer S. Ahluwalia, Tian Liu, Martin Kroh, Aldo Rustichini, Giorgia Girotto, Cristina Venturini, Lili Milani, Jennifer A. Smith, Ginevra Biino, Tessel E. Galesloot, Michael A. Horan, Gerardus A. Meddens, James F. Wilson, Francesco Cucca, Peter Vollenweider, Erika Salvi, P. J. van der Most, Jari Lahti, Campbell A, David Laibson, Andrew Bakshi, Wolfgang Hoffmann, Tomi Mäki-Opas, Andreas J. Forstner, C M van Duijn, Nicholas G. Martin, Jonathan Marten, Ute Bültmann, Olli T. Raitakari, David A. Bennett, A.G. Uitterlinden, J. E. De Neve, Ingrid B. Borecki, WD Hill, Bo Jacobsson, Antti Latvala, Katri Räikkönen, Michael B. Miller, Jonathan P. Beauchamp, S. J. van der Lee, Ilja Demuth, Stavroula Kanoni, Veronique Vitart, Elina Hyppönen, N. Eklund, Francesco P. Cappuccio, Robert F. Krueger, Maria Pina Concas, Jaime Derringer, F. J.A. Van Rooij, Helena Schmidt, Patrick J. F. Groenen, Valur Emilsson, Rico Rueedi, Aysu Okbay, Georg Homuth, Edith Hofer, W. E. R. Ollier, Hannah Campbell, Paolo Gasparini, Mark Alan Fontana, Magnus Johannesson, Seppo Koskinen, Christopher F. Chabris, Jouke-Jan Hottenga, Christine Meisinger, Kari Stefansson, Jun Ding, Tia Sorensen, Brenda W.J.H. Penninx, Michelle N. Meyer, James J. Lee, Diego Vozzi, Gonneke Willemsen, K. Petrovic, Sarah E. Medland, Mary F. Feitosa, Henning Tiemeier, L. J. Launer, William G. Iacono, Massimo Mangino, Tune H. Pers, S. E. Baumeister, Christopher Oldmeadow, Grant W. Montgomery, Marjo-Riitta Järvelin, Jaakko Kaprio, Catharine R. Gale, S.F.W. Meddens, Kevin Thom, Klaus Berger, Pablo V. Gejman, Lude Franke, Gyda Bjornsdottir, Daniel J. Benjamin, Steven F. Lehrer, Krista Fischer, Alan R. Sanders, S. Ulivi, Katharina E. Schraut, Tim D. Spector, Amy Hofman, Matt McGue, Terho Lehtimäki, D. C. Liewald, Hans Bisgaard, L. Eisele, Astanand Jugessur, George Davey Smith, T.B. Harris, A.R. Thurik, Cornelius A. Rietveld, David Schlessinger, Z. Kutalik, David J. Porteous, Lynne J. Hocking, N J Timpson, A. Palotie, Lambertus A. Kiemeney, Ian J. Deary, Sharon L.R. Kardia, Peter K. Joshi, Nilesh J. Samani, Michael A. Province, Börge Schmidt, Richa Gupta, Carmen Amador, Erin B. Ware, Joyce Y. Tung, Ioanna-Panagiota Kalafati, Lars Bertram, Caroline Hayward, P. van der Harst, Penelope A. Lind, Kadri Kaasik, N.A. Furlotte, Sarah E. Harris, B. St Pourcain, Susan M. Ring, Zhihong Zhu, Alexander Teumer, Behrooz Z. Alizadeh, Judith M. Vonk, Blair H. Smith, A Payton, Wouter J. Peyrot, Jacob Gratten, Douglas F. Levinson, C Gieger, Leanne M. Hall, Andrew Heath, Mario Pirastu, Peter Eibich, Nancy L. Pedersen, Ronny Myhre, Antonio Terracciano, David M. Evans, Raymond A. Poot, Uwe Völker, Dorret I. Boomsma, Clemens Baumbach, Unnur Thorsteinsdottir, Ivana Kolcic, Jia-Shu Yang, Dalton Conley, A. A. Vinkhuyzen, Danielle Posthuma, Karl-Oskar Lindgren, Olga Rostapshova, Jonas Bacelis, Daniele Cusi, Yong Qian, Bjarni Gunnarsson, George McMahon, Elizabeth G. Holliday, Pamela A. F. Madden, David A. Hinds, David Cesarini, Jianxin Shi, Najaf Amin, Dale R. Nyholt, Applied Economics, Epidemiology, Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Groningen Research Institute for Asthma and COPD (GRIAC), Aletta Jacobs School of Public Health, Public Health Research (PHR), Stem Cell Aging Leukemia and Lymphoma (SALL), Cardiovascular Centre (CVC), Amsterdam Neuroscience - Complex Trait Genetics, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, EMGO - Mental health, Complex Trait Genetics, Biological Psychology, Marioni, RE, Ritchie, SJ, Joshi, PK, Hagenaars, SP, Hypponen, E, Benjamin, DJ, Social Science Genetic Association Consortium, Marioni, Re, Ritchie, Sj, Joshi, Pk, Hagenaars, Sp, Okbay, A, Fischer, K, Adams, Mj, Hill, Wd, Davies, G, Nagy, R, Amador, C, Läll, K, Metspalu, A, Liewald, Dc, Campbell, A, Wilson, Jf, Hayward, C, Esko, T, Porteous, Dj, Gale, Cr, Deary, Ij, Beauchamp, Jp, Fontana, Ma, Lee, Jj, Pers, Th, Rietveld, Ca, Turley, P, Chen, Gb, Emilsson, V, Meddens, Sf, Oskarsson, S, Pickrell, Jk, Thom, K, Timshel, P, de Vlaming, R, Abdellaoui, A, Ahluwalia, T, Bacelis, J, Baumbach, C, Bjornsdottir, G, Brandsma, Jh, Concas, MARIA PINA, Derringer, J, Furlotte, Na, Galesloot, Te, Girotto, Giorgia, Gupta, R, Hall, Lm, Harris, Se, Hofer, E, Horikoshi, M, Huffman, Je, Kaasik, K, Kalafati, Ip, Karlsson, R, Kong, A, Lahti, J, van der Lee, Sj, de Leeuw, C, Lind, Pa, Lindgren, Ko, Liu, T, Mangino, M, Marten, J, Mihailov, E, Miller, Mb, van der Most, Pj, Oldmeadow, C, Payton, A, Pervjakova, N, Peyrot, Wj, Qian, Y, Raitakari, O, Rueedi, R, Salvi, E, Schmidt, B, Schraut, Ke, Shi, J, Smith, Av, Poot, Ra, St Pourcain, B, Teumer, A, Thorleifsson, G, Verweij, N, Vuckovic, Dragana, Wellmann, J, Westra, Hj, Yang, J, Zhao, W, Zhu, Z, Alizadeh, Bz, Amin, N, Bakshi, A, Baumeister, Se, Biino, G, Bønnelykke, K, Boyle, Pa, Campbell, H, Cappuccio, Fp, De Neve, Je, Deloukas, P, Demuth, I, Ding, J, Eibich, P, Eisele, L, Eklund, N, Evans, Dm, Faul, Jd, Feitosa, Mf, Forstner, Aj, Gandin, Ilaria, Gunnarsson, B, Halldórsson, Bv, Harris, Tb, Heath, Ac, Hocking, Lj, Holliday, Eg, Homuth, G, Horan, Ma, Hottenga, Jj, de Jager, Pl, Jugessur, A, Kaakinen, Ma, Kähönen, M, Kanoni, S, Keltigangas Järvinen, L, Kiemeney, La, Kolcic, I, Koskinen, S, Kraja, At, Kroh, M, Kutalik, Z, Latvala, A, Launer, Lj, Lebreton, Mp, Levinson, Df, Lichtenstein, P, Lichtner, P, Loukola, A, Madden, Pa, Mägi, R, Mäki Opas, T, Marques Vidal, P, Meddens, Ga, Mcmahon, G, Meisinger, C, Meitinger, T, Milaneschi, Y, Milani, L, Montgomery, Gw, Myhre, R, Nelson, Cp, Nyholt, Dr, Ollier, We, Palotie, A, Paternoster, L, Pedersen, Nl, Petrovic, Ke, Räikkönen, K, Ring, Sm, Robino, Antonietta, Rostapshova, O, Rudan, I, Rustichini, A, Salomaa, V, Sanders, Ar, Sarin, Ap, Schmidt, H, Scott, Rj, Smith, Bh, Smith, Ja, Staessen, Ja, Steinhagen Thiessen, E, Strauch, K, Terracciano, A, Tobin, Md, Ulivi, Sheila, Vaccargiu, S, Quaye, L, van Rooij, Fj, Venturini, C, Vinkhuyzen, Aa, Völker, U, Völzke, H, Vonk, Jm, Vozzi, Diego, Waage, J, Ware, Eb, Willemsen, G, Attia, Jr, Bennett, Da, Berger, K, Bertram, L, Bisgaard, H, Boomsma, Di, Borecki, Ib, Bultmann, U, Chabris, Cf, Cucca, F, Cusi, D, Dedoussis, Gv, van Duijn, Cm, Eriksson, Jg, Franke, B, Franke, L, Gasparini, Paolo, Gejman, Pv, Gieger, C, Grabe, Hj, Gratten, J, Groenen, Pj, Gudnason, V, van der Harst, P, Hinds, Da, Hoffmann, W, Iacono, Wg, Jacobsson, B, Järvelin, Mr, Jöckel, Kh, Kaprio, J, Kardia, Sl, Lehtimäki, T, Lehrer, Sf, Magnusson, Pk, Martin, Ng, Mcgue, M, Pendleton, N, Penninx, Bw, Perola, M, Pirastu, Nicola, Pirastu, M, Polasek, O, Posthuma, D, Power, C, Province, Ma, Samani, Nj, Schlessinger, D, Schmidt, R, Sørensen, Ti, Spector, Td, Stefansson, K, Thorsteinsdottir, U, Thurik, Ar, Timpson, Nj, Tiemeier, H, Tung, Jy, Uitterlinden, Ag, Vitart, V, Vollenweider, P, Weir, Dr, Wright, Af, Conley, Dc, Krueger, Rf, Smith, Gd, Hofman, A, Laibson, Di, Medland, Se, Meyer, Mn, Johannesson, M, Visscher, Pm, Koellinger, Pd, Cesarini, D, and Benjamin, Dj

Subjects
Netherlands Twin Register (NTR), 0301 basic medicine, Male, Parents, education: longevity: prediction: polygenic score [genetics], Multifactorial Inheritance, polygenic, Lebenserwartung, Cohort Studies, 0302 clinical medicine, Databases, Genetic, Medicine, genetics, polygenic score, longevity, education, gene, Soziales und Gesundheit, media_common, Aged, 80 and over, education, Multidisciplinary, Longevity, Middle Aged, Biobank, humanities, 3. Good health, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Cohort, Educational Status, Female, Cohort study, Estonia, education, longevity, polygenic, Offspring, media_common.quotation_subject, Kultursektor, Prognose, Lernen, Lower risk, Education, 03 medical and health sciences, longevity, SDG 3 - Good Health and Well-being, Commentaries, Polygenic score, Journal Article, Genetics, Humans, Non-Profit-Sektor, Genetic Association Studies, Aged, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, ta1184, Genetic Variation, prediction, Educational attainment, United Kingdom, Gesundheitsstatistik, 030104 developmental biology, Genetic epidemiology, Scotland, Gesundheitszustand, Genetische Forschung, business, Prediction, Bildung, 030217 neurology & neurosurgery, Demography
Abstract

Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total n deaths = 79,702) and ∼2.4% lower risk for fathers (total n deaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity. Marioni RE, Ritchie SJ, Joshi PK, Hagenaars SP, Okbay A, Fischer K, Adams MJ, Hill WD, Davies G, Social Science Genetic Association Consortium, Nagy R, Amador C, Läll K, Metspalu A, Liewald DC, Campbell A, Wilson JF, Hayward C, Esko T, Porteous DJ, Proceedings of the National Academy of Sciences of the United States of America, 2016, vol. 113, no. 47, pp. 13366-13371, 2016 Refereed/Peer-reviewed

Published
2016
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37. Essays on the Intersection of Economics and Biology

Author

Rietveld, Niels, Thurik, Roy, Koellinger, PD, Groenen, Patrick, Hofman, Bert, Bleichrodt, Han, Tiemeier, Henning, Webbink, Dinand, and Applied Economics

Subjects
SDG 3 - Good Health and Well-being
Abstract

Biological economics is the interdisciplinary research field in which the interaction between human biology and economics is investigated and in which human beings are primarily seen as biological organisms. This thesis adopts a perspective in which certain individual economic choices and outcomes are connected to individual biological characteristics. The value of biological economics is at least threefold. First, knowledge about biological predispositions to economic choices and outcomes improves our understanding of the causes and consequences of individual differences. Second, biological measures known to be associated with economic choices and outcomes could be used in (otherwise non-biological) empirical work as control variables or instrumental variables. Third, information about economic predisposition to biological states and outcomes may result in targeted interventions to prevent undesired outcomes. Although it has been known for already quite some time that many economic choices and outcomes are heritable, no robust, replicable genetic predisposition to them had been found. This thesis shows that it is possible to find such associations, robustly and replicable, with relatively crude methods in combination with statistical rigor. This thesis also presents evidence that self-employed individuals are generally healthier than wage workers and that the selection of comparatively healthier individuals into self-employment accounts for the cross-sectional association. It presents tentative evidence that, if anything, engaging in self-employment is bad for one’s health. In addition, popular ideas that entrepreneurship is a particular good occupation for dyslectic and left-handed individuals could not be substantiated.

Published
2014

38. Molecular Genetics and Hormones: New Frontiers in Entrepreneurship Research

Author

van der Loos, MJHM (Matthijs), Thurik, Roy, Groenen, Patrick, Hofman, Bert, Bleichrodt, Han, Johannesson, M, Tiemeier, Henning, Koellinger, PD, and Applied Economics

Abstract

Recent studies suggest that entrepreneurship is partly heritable, but are unable to pinpoint the specific genes involved. This thesis presents results from novel research aiming to identify genes associated with entrepreneurship using genetic data on the molecular level. In addition, the relationship between testosterone and entrepreneurship is examined since genes may exert their influence through this hormone. The thesis starts by reviewing candidate gene studies that test a pre-specified set of genes for association, but which often fail to replicate. An example within the setting of entrepreneurship research is provided to illustrate this last point. Next, the genome-wide association study (GWAS) design is presented that scans the entire genome for associations. However, due to multiple testing, GWAS requires very large sample sizes to establish robust associations and we perform a simulation study to estimate the minimum sample size needed for a GWAS on entrepreneurship. The following part reports evidence that entrepreneurship is partly heritable and around half of the heritability is accounted for by actual molecular genetic data. However, a GWAS on entrepreneurship does not identify robustly associated genes and prediction exercises show that it is currently impossible to predict entrepreneurship solely from molecular genetic data. In the final part, we show that, in contrast to earlier findings, testosterone is not associated with entrepreneurship. Taken as a whole, the results suggest that entrepreneurship is likely to be influenced by hundreds if not thousands of genes with a very small effect size each, implying that very large sample sizes will be needed in future research to discover associated genes. Most importantly, this thesis may serve as a practical guide for studying the molecular genetics of other economic variables. In conclusion, this thesis helps to build the foundations for a novel research field that integrates molecular genetics into economics.

Published
2013

39. Genome-wide analysis identifies 12 loci influencing human reproductive behavior

Author

Ozren Polasek, Bo Jacobsson, Eleonora Porcu, Vinicius Tragante, Joel Eriksson, Jie Yao, Mika Kähönen, Mark Alan Fontana, Stefania Cappellani, J. Viikari, Rick Jansen, Crysovalanto Mamasoula, Linda Broer, Tamara B. Harris, Ellen A. Nohr, Genevieve Lachance, Johan G. Eriksson, Nicholas Eriksson, Rico Rueedi, Francesco Cucca, Jaakko Kaprio, Nicholas J. Timpson, George Dedoussis, Matt McGue, Per Magnus, Klaus Berger, Olli T. Raitakari, Cornelia M. van Duijn, Brenda W.J.H. Penninx, Jing Hua Zhao, Peter Eibich, Sheila Ulivi, Hugoline G. de Haan, Ronny Myhre, Ruth McQuillan, Florian Kronenberg, Markus Perola, Klaus Bønnelykke, Robert Karlsson, Martina La Bianca, Paul Mitchell, Ian J. Deary, Melinda Mills, Teresa Nutile, Patrick J. F. Groenen, Stacey A. Missmer, Nicholas G. Martin, Panos Deloukas, Mario Pirastu, Lindsay K. Matteson, Robert Luben, Veikko Salomaa, Renée de Mutsert, Chris Power, Nir Barzilai, Annette Kifley, Hamdi Mbarek, Denis A. Evans, Erica P. Gunderson, Tim D. Spector, Anke Tönjes, Michela Traglia, Claire Monnereau, Karin Halina Greiser, Sharon L.R. Kardia, John M. Starr, Peter K. Joshi, Sandra Lai, Doris Stöckl, James J. Lee, Heather J. Cordell, Andrew Bakshi, Nicholas J. Wareham, David C. Liewald, P Koponen, Paul M. Ridker, Joyce Y. Tung, Ilaria Gandin, Kauko Heikkilä, Johannes Haerting, Gonneke Willemsen, Janet W. Rich-Edwards, Andrew C. Heath, Astanand Jugessur, John L. Hopper, Stefan Kiechl, Henry Völzke, Daniela Ruggiero, John R. B. Perry, Dan Mellström, Simon R. Cox, Yasaman Saba, Magnus Johannesson, Ginevra Biino, David Schlessinger, Kirsi Auro, Dennis O. Mook-Kanamori, Christa Meisinger, Igor Rudan, Audrey J. Gaskins, Lars Bertram, Roy Thurik, Laura M. Yerges-Armstrong, Caterina Barbieri, Katri Räikkönen, Lawrence F. Bielak, Aviv Bergman, Philipp Koellinger, Ronald de Vlaming, Tian Liu, Johannes W. A. Smit, Peter Kovacs, Vincent W. V. Jaddoe, Jennifer A. Smith, Sven Bergmann, Inga Prokopenko, Xiuqing Guo, Marina Ciullo, Krina T. Zondervan, Marcel den Hoed, Daniel J. Benjamin, Kathryn Roll, Alan F. Wright, Helena Schmidt, William G. Iacono, Jie Jin Wang, Harold Snieder, Juho Wedenoja, Tarunveer S. Ahluwalia, David R. Weir, Ken K. Ong, Daniela Toniolo, Ruifang Li-Gao, Evelin Mihailov, Edith Hofer, Leslie J. Raffel, Daniel I. Chasman, Alexander Kluttig, Bernard Keavney, Eco J. C. de Geus, Kathleen A. Ryan, Kristin L. Ayers, Lude Franke, S. Fleur W. Meddens, Alison Pattie, Jornt J. Mandemakers, Eva Albrecht, David Cesarini, Beverley Balkau, Grant W. Montgomery, Michael Stumvoll, Ahmad Vaez, Michael B. Miller, Najaf Amin, Gyda Bjornsdottir, Cecile Lecoeur, Enes Makalic, Marc Jan Bonder, Terho Lehtimäki, Albert Hofman, Loic Yengo, Lynda M. Rose, Lisette Stolk, Juergen Wellmann, Gail Davies, Eero Kajantie, Nicole Schupf, Hans Bisgaard, Unnur Thorsteinsdottir, Konstantin Strauch, Ivana Kolcic, Lili Milani, Chunyan He, Claes Ohlsson, Yongmei Liu, Gil Atzmon, Janine F. Felix, Christian Gieger, Mike A. Nalls, Riitta Luoto, Nicola Barban, Philippe Froguel, Daniel F. Schmidt, Dorret I. Boomsma, Harry Campbell, Xia Shen, Vasiliki Lagou, Danny Ben-Avraham, Veronique Vitart, Ioanna P. Kalafati, Kari Stefansson, Daria V. Zhernakova, Constance Turman, Julie E. Buring, Johannes Waage, James F. Wilson, Maria Pina Concas, Zoltán Kutalik, Peter Willeit, Jørn Olsen, Dan Rujescu, Caroline Hayward, Penelope A. Lind, George McMahon, Elizabeth G. Holliday, Ilja M. Nolte, Fahimeh Falahi, Minh Bui, Gudmar Thorleifsson, Patrick F. McArdle, Cinzia Sala, Alana Cavadino, Rossella Sorice, Wei Zhao, Andres Metspalu, Sander W. van der Laan, Stavroula Kanoni, Elina Hyppönen, Morris A. Swertz, Simona Vaccargiu, Felix C. Tropf, Michael Lucht, Susan M. Ring, Elizabeth A. Streeten, Reinhold Schmidt, Augustine Kong, Johann Willeit, Patricia A. Peyser, Jessica D. Faul, Patrik K. E. Magnusson, Tõnu Esko, Antonietta Robino, Lavinia Paternoster, Peter J. van der Most, Kumar B. Rajan, George Davey-Smith, Dragana Vuckovic, Hans J. Grabe, Jari Lahti, Giorgia Girotto, Jorge E. Chavarro, Robert F. Krueger, Hongyan Huang, Georg Homuth, Paolo Gasparini, Sarah E. Medland, Gert G. Wagner, Peter Kraft, André G. Uitterlinden, Cornelius A. Rietveld, Howard Andrews, Cecilia M. Lindgren, Peter Vollenweider, Perry, John [0000-0001-6483-3771], Zhao, Jing Hua [0000-0003-4930-3582], Luben, Robert [0000-0002-5088-6343], Ong, Kenneth [0000-0003-4689-7530], Wareham, Nicholas [0000-0003-1422-2993], Apollo - University of Cambridge Repository, BARBAN N, Rick Jansen, Ronald de Vlaming, Ahmad Vaez, Jornt J Mandemaker, Felix C Tropf, Xia Shen, James F Wilson, Daniel I Chasman, Ilja M Nolte, Vinicius Tragante, Sander W van der Laan, John R B Perry, Augustine Kong, BIOS Consortium, Tarunveer S Ahluwalia, Eva Albrecht, Laura Yerges-Armstrong, Gil Atzmon, Kirsi Auro, Kristin Ayer, Andrew Bakshi, Danny Ben-Avraham, Klaus Berger, Aviv Bergman, Lars Bertram, Lawrence F Bielak, Gyda Bjornsdottir, Marc Jan Bonder, Linda Broer, Minh Bui, Caterina Barbieri, Alana Cavadino, Jorge E Chavarro, Constance Turman, Maria Pina Conca, Heather J Cordell, Gail Davie, Peter Eibich, Nicholas Eriksson, Tõnu Esko, Joel Eriksson, Fahimeh Falahi, Janine F Felix, Mark Alan Fontana, Lude Franke, Ilaria Gandin, Audrey J Gaskin, Christian Gieger, Erica P Gunderson, Xiuqing Guo, Caroline Hayward, Chunyan He, Edith Hofer, Hongyan Huang, Peter K Joshi, Stavroula Kanoni, Robert Karlsson, Stefan Kiechl, Annette Kifley, Alexander Kluttig, Peter Kraft, Vasiliki Lagou, Cecile Lecoeur, Jari Lahti, Ruifang Li-Gao, Penelope A Lind, Tian Liu, Enes Makalic, Crysovalanto Mamasoula, Lindsay Matteson, Hamdi Mbarek, Patrick F McArdle, George McMahon, S Fleur W Medden, Evelin Mihailov, Mike Miller, Stacey A Missmer, Claire Monnereau, Peter J van der Most, Ronny Myhre, Mike A Nall, Teresa Nutile, Ioanna Panagiota Kalafati, Eleonora Porcu, Inga Prokopenko, Kumar B Rajan, Janet Rich-Edward, Cornelius A Rietveld, Antonietta Robino, Lynda M Rose, Rico Rueedi, Kathleen A Ryan, Yasaman Saba, Daniel Schmidt, Jennifer A Smith, Lisette Stolk, Elizabeth Streeten, Anke Tönje, Gudmar Thorleifsson, Sheila Ulivi, Juho Wedenoja, Juergen Wellmann, Peter Willeit, Jie Yao, Loic Yengo, Jing Hua Zhao, Wei Zhao, Daria V Zhernakova, Najaf Amin, Howard Andrew, Beverley Balkau, Nir Barzilai, Sven Bergmann, Ginevra Biino, Hans Bisgaard, Klaus Bønnelykke, Dorret I Boomsma, Julie E Buring, Harry Campbell, Stefania Cappellani, Marina Ciullo, Simon R Cox, Francesco Cucca, Daniela Toniolo, George Davey-Smith, Ian J Deary, George Dedoussi, Panos Delouka, Cornelia M van Duijn, Eco J C de Geu, Johan G Eriksson, Denis A Evan, Jessica D Faul, Cinzia Felicita Sala, Philippe Froguel, Paolo Gasparini, Giorgia Girotto, Hans-Jörgen Grabe, Karin Halina Greiser, Patrick J F Groenen, Hugoline G de Haan, Johannes Haerting, Tamara B Harri, Andrew C Heath, Kauko Heikkilä, Albert Hofman, Georg Homuth, Elizabeth G Holliday, John Hopper, Elina Hyppönen, Bo Jacobsson, Vincent W V Jaddoe, Magnus Johannesson, Astanand Jugessur, Mika Kähönen, Eero Kajantie, Sharon L R Kardia, Bernard Keavney, Ivana Kolcic, Päivikki Koponen, Peter Kovac, Florian Kronenberg, Zoltan Kutalik, Martina La Bianca, Genevieve Lachance, William G Iacono, Sandra Lai, Terho Lehtimäki, David C Liewald, LifeLines Cohort Study, Cecilia M Lindgren, Yongmei Liu, Robert Luben, Michael Lucht, Riitta Luoto, Per Magnu, Patrik K E Magnusson, Nicholas G Martin, Matt McGue, Ruth McQuillan, Sarah E Medland, Christa Meisinger, Dan Mellström, Andres Metspalu, Michela Traglia, Lili Milani, Paul Mitchell, Grant W Montgomery, Dennis Mook-Kanamori, Renée de Mutsert, Ellen A Nohr, Claes Ohlsson, Jørn Olsen, Ken K Ong, Lavinia Paternoster, Alison Pattie, Brenda W J H Penninx, Markus Perola, Patricia A Peyser, Mario Pirastu, Ozren Polasek, Chris Power, Jaakko Kaprio, Leslie J Raffel, Katri Räikkönen, Olli Raitakari, Paul M Ridker, Susan M Ring, Kathryn Roll, Igor Rudan, Daniela Ruggiero, Dan Rujescu, Veikko Salomaa, David Schlessinger, Helena Schmidt, Reinhold Schmidt, Nicole Schupf, Johannes Smit, Rossella Sorice, Tim D Spector, John M Starr, Doris Stöckl, Konstantin Strauch, Michael Stumvoll, Morris A Swertz, Unnur Thorsteinsdottir, A Roy Thurik, Nicholas J Timpson, Joyce Y Tung, André G Uitterlinden, Simona Vaccargiu, Jorma Viikari, Veronique Vitart, Henry Völzke, Peter Vollenweider, Dragana Vuckovic, Johannes Waage, Gert G Wagner, Jie Jin Wang, Nicholas J Wareham, David R Weir, Gonneke Willemsen, Johann Willeit, Alan F Wright, Krina T Zondervan, Kari Stefansson, Robert F Krueger, James J Lee, Daniel J Benjamin, David Cesarini, Philipp D Koellinger, Marcel den Hoed, Harold Snieder & Melinda C Mills, Barban, N, Jansen, R, de Vlaming, R, Vaez, A, Mandemakers, Jj, Tropf, Fc, Shen, X, Wilson, Jf, Chasman, Di, Nolte, Im, Tragante, V, van der Laan, Sw, Perry, Jr, Kong, A, Ahluwalia, T, Albrecht, E, Yerges Armstrong, L, Atzmon, G, Auro, K, Ayers, K, Bakshi, A, Ben Avraham, D, Berger, K, Bergman, A, Bertram, L, Bielak, Lf, Bjornsdottir, G, Bonder, Mj, Broer, L, Bui, M, Barbieri, CATERINA MARIA, Cavadino, A, Chavarro, Je, Turman, C, Concas, MARIA PINA, Cordell, Hj, Davies, G, Eibich, P, Eriksson, N, Esko, T, Eriksson, J, Falahi, F, Felix, Jf, Fontana, Ma, Franke, L, Gandin, Ilaria, Gaskins, Aj, Gieger, C, Gunderson, Ep, Guo, X, Hayward, C, He, C, Hofer, E, Huang, H, Joshi, Pk, Kanoni, S, Karlsson, R, Kiechl, S, Kifley, A, Kluttig, A, Kraft, P, Lagou, V, Lecoeur, C, Lahti, J, Li Gao, R, Lind, Pa, Liu, T, Makalic, E, Mamasoula, C, Matteson, L, Mbarek, H, Mcardle, Pf, Mcmahon, G, Meddens, Sf, Mihailov, E, Miller, M, Missmer, Sa, Monnereau, C, van der Most, Pj, Myhre, R, Nalls, Ma, Nutile, T, Kalafati, Ip, Porcu, E, Prokopenko, I, Rajan, Kb, Rich Edwards, J, Rietveld, Ca, Robino, Antonietta, Rose, Lm, Rueedi, R, Ryan, Ka, Saba, Y, Schmidt, D, Smith, Ja, Stolk, L, Streeten, E, Tönjes, A, Thorleifsson, G, Ulivi, Sheila, Wedenoja, J, Wellmann, J, Willeit, P, Yao, J, Yengo, L, Zhao, Jh, Zhao, W, Zhernakova, Dv, Amin, N, Andrews, H, Balkau, B, Barzilai, N, Bergmann, S, Biino, G, Bisgaard, H, Bønnelykke, K, Boomsma, Di, Buring, Je, Campbell, H, Cappellani, Stefania, Ciullo, M, Cox, Sr, Cucca, F, Toniolo, D, Davey Smith, G, Deary, Ij, Dedoussis, G, Deloukas, P, van Duijn, Cm, de Geus, Ej, Eriksson, Jg, Evans, Da, Faul, Jd, Sala, Cf, Froguel, P, Gasparini, Paolo, Girotto, Giorgia, Grabe, Hj, Greiser, Kh, Groenen, Pj, de Haan, Hg, Haerting, J, Harris, Tb, Heath, Ac, Heikkilä, K, Hofman, A, Homuth, G, Holliday, Eg, Hopper, J, Hyppönen, E, Jacobsson, B, Jaddoe, Vw, Johannesson, M, Jugessur, A, Kähönen, M, Kajantie, E, Kardia, Sl, Keavney, B, Kolcic, I, Koponen, P, Kovacs, P, Kronenberg, F, Kutalik, Z, LA BIANCA, Martina, Lachance, G, Iacono, Wg, Lai, S, Lehtimäki, T, Liewald, Dc, Lindgren, Cm, Liu, Y, Luben, R, Lucht, M, Luoto, R, Magnus, P, Magnusson, Pk, Martin, Ng, Mcgue, M, Mcquillan, R, Medland, Se, Meisinger, C, Mellström, D, Metspalu, A, Traglia, Michela, Milani, L, Mitchell, P, Montgomery, Gw, Mook Kanamori, D, de Mutsert, R, Nohr, Ea, Ohlsson, C, Olsen, J, Ong, Kk, Paternoster, L, Pattie, A, Penninx, Bw, Perola, M, Peyser, Pa, Pirastu, M, Polasek, O, Power, C, Kaprio, J, Raffel, Lj, Räikkönen, K, Raitakari, O, Ridker, Pm, Ring, Sm, Roll, K, Rudan, I, Ruggiero, D, Rujescu, D, Salomaa, V, Schlessinger, D, Schmidt, H, Schmidt, R, Schupf, N, Smit, J, Sorice, R, Spector, Td, Starr, Jm, Stöckl, D, Strauch, K, Stumvoll, M, Swertz, Ma, Thorsteinsdottir, U, Thurik, Ar, Timpson, Nj, Tung, Jy, Uitterlinden, Ag, Vaccargiu, S, Viikari, J, Vitart, V, Völzke, H, Vollenweider, P, Vuckovic, Dragana, Waage, J, Wagner, Gg, Wang, Jj, Wareham, Nj, Weir, Dr, Willemsen, G, Willeit, J, Wright, Af, Zondervan, Kt, Stefansson, K, Krueger, Rf, Lee, Jj, Benjamin, Dj, Cesarini, D, Koellinger, Pd, den Hoed, M, Snieder, H, Mills, Mc, Sociology/ICS, Life Course Epidemiology (LCE), Isotope Research, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Barban, Nicola, Jansen, Rick, De Vlaming, Ronald, Vaez, Ahmad, Hyppönen, Elina, Mills, Melinda C, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, EMGO - Mental health, Applied Economics, Public Health, Internal Medicine, Erasmus MC other, Epidemiology, Econometrics, Pediatrics, EMGO+ - Lifestyle, Overweight and Diabetes, Complex Trait Genetics, and Biological Psychology

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), PROTEIN, WASS, Genome-wide association study, Reproductive Behavior, MOUSE, Genome-wide association studies, GWAS, reproductive behavior, fertility, 0302 clinical medicine, G1 PHASE, Pregnancy, Genetics & Heredity, Genetics, HUMAN-DISEASES, Reproduction, Human Reproduction, 11 Medical And Health Sciences, ASSOCIATION, Genome-Wide, POLYCYSTIC-OVARY-SYNDROME, Sociologie van Consumptie en Huishoudens, Parity, Phenotype, Behavioural genetics, Medical genetics, Female, BIOS Consortium, FOS: Medical biotechnology, Life Sciences & Biomedicine, Maternal Age, Infertility, medicine.medical_specialty, GENE PRIORITIZATION, Quantitative Trait Loci, Sociology of Consumption and Households, Quantitative trait locus, Biology, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, AGE, QUALITY-CONTROL, medicine, Journal Article, Life Science, SNP, Humans, gene, reproductive, behaviour, Science & Technology, ta1184, 06 Biological Sciences, medicine.disease, Genetic architecture, human reproductive behavior, 030104 developmental biology, Fertility, Human genome, Birth Order, 030217 neurology & neurosurgery, LifeLines Cohort Study, Developmental Biology, genome-wide analysis, Genome-Wide Association Study
Abstract

Barban N, Jansen R, de Vlaming R, Vaez A, Mandemakers JJ, Tropf FC, Shen X, Wilson JF, Chasman DI, Nolte IM, Tragante V, van der Laan SW, Perry JR, Kong A; BIOS Consortium, Ahluwalia TS, Albrecht E, Yerges-Armstrong L, Atzmon G, Auro K, Ayers K, Bakshi A, Ben-Avraham D, Berger K, Bergman A, Bertram L, Bielak LF, Bjornsdottir G, Bonder MJ, Broer L, Bui M, Barbieri C, Cavadino A, Chavarro JE, Turman C, Concas MP, Cordell HJ, Davies G, Eibich P, Eriksson N, Esko T, Eriksson J, Falahi F, Felix JF, Fontana MA, Franke L, Gandin I, Gaskins AJ, Gieger C, Gunderson EP, Guo X, Hayward C, He C, Hofer E, Huang H, Joshi PK, Kanoni S, Karlsson R, Kiechl S, Kifley A, Kluttig A, Kraft P, Lagou V, Lecoeur C, Lahti J, Li-Gao R, Lind PA, Liu T, Makalic E, Mamasoula C, Matteson L, Mbarek H, McArdle PF, McMahon G, Meddens SF, Mihailov E, Miller M, Missmer SA, Monnereau C, van der Most PJ, Myhre R, Nalls MA, Nutile T, Kalafati IP, Porcu E, Prokopenko I, Rajan KB, Rich-Edwards J, Rietveld CA, Robino A, Rose LM, Rueedi R, Ryan KA, Saba Y, Schmidt D, Smith JA, Stolk L, Streeten E, Tönjes A, Thorleifsson G, Ulivi S, Wedenoja J, Wellmann J, Willeit P, Yao J, Yengo L, Zhao JH, Zhao W, Zhernakova DV, Amin N, Andrews H, Balkau B, Barzilai N, Bergmann S, Biino G, Bisgaard H, Bønnelykke K, Boomsma DI, Buring JE, Campbell H, Cappellani S, Ciullo M, Cox SR, Cucca F, Toniolo D, Davey-Smith G, Deary IJ, Dedoussis G, Deloukas P, van Duijn CM, de Geus EJ, Eriksson JG, Evans DA, Faul JD, Sala CF, Froguel P, Gasparini P, Girotto G, Grabe HJ, Greiser KH, Groenen PJ, de Haan HG, Haerting J, Harris TB, Heath AC, Heikkilä K, Hofman A, Homuth G, Holliday EG, Hopper J, Hyppönen E, Jacobsson B, Jaddoe VW, Johannesson M, Jugessur A, Kähönen M, Kajantie E, Kardia SL, Keavney B, Kolcic I, Koponen P, Kovacs P, Kronenberg F, Kutalik Z, La Bianca M, Lachance G, Iacono WG, Lai S, Lehtimäki T, Liewald DC; LifeLines Cohort Study, Lindgren CM, Liu Y, Luben R, Lucht M, Luoto R, Magnus P, Magnusson PK, Martin NG, McGue M, McQuillan R, Medland SE, Meisinger C, Mellström D, Metspalu A, Traglia M, Milani L, Mitchell P, Montgomery GW, Mook-Kanamori D, de Mutsert R, Nohr EA, Ohlsson C, Olsen J, Ong KK, Paternoster L, Pattie A, Penninx BW, Perola M, Peyser PA, Pirastu M, Polasek O, Power C, Kaprio J, Raffel LJ, Räikkönen K, Raitakari O, Ridker PM, Ring SM, Roll K, Rudan I, Ruggiero D, Rujescu D, Salomaa V, Schlessinger D, Schmidt H, Schmidt R, Schupf N, Smit J, Sorice R, Spector TD, Starr JM, Stöckl D, Strauch K, Stumvoll M, Swertz MA, Thorsteinsdottir U, Thurik AR, Timpson NJ, Tung JY, Uitterlinden AG, Vaccargiu S, Viikari J, Vitart V, Völzke H, Vollenweider P, Vuckovic D, Waage J, Wagner GG, Wang JJ, Wareham NJ, Weir DR, Willemsen G, Willeit J, Wright AF, Zondervan KT, Stefansson K, Krueger RF, Lee JJ, Benjamin DJ, Cesarini D, Koellinger PD, den Hoed M, Snieder H, Mills MC.

Published
2016
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40. Associations between common genetic variants and income provide insights about the socio-economic health gradient.

Author

Kweon H, Burik CAP, Ning Y, Ahlskog R, Xia C, Abner E, Bao Y, Bhatta L, Faquih TO, de Feijter M, Fisher P, Gelemanović A, Giannelis A, Hottenga JJ, Khalili B, Lee Y, Li-Gao R, Masso J, Myhre R, Palviainen T, Rietveld CA, Teumer A, Verweij RM, Willoughby EA, Agerbo E, Bergmann S, Boomsma DI, Børglum AD, Brumpton BM, Davies NM, Esko T, Gordon SD, Homuth G, Ikram MA, Johannesson M, Kaprio J, Kidd MP, Kutalik Z, Kwong ASF, Lee JJ, Luik AI, Magnus P, Marques-Vidal P, Martin NG, Mook-Kanamori DO, Mortensen PB, Oskarsson S, Pedersen EM, Polašek O, Rosendaal FR, Smart MC, Snieder H, van der Most PJ, Vollenweider P, Völzke H, Willemsen G, Beauchamp JP, DiPrete TA, Linnér RK, Lu Q, Morris TT, Okbay A, Harden KP, Abdellaoui A, Hill WD, de Vlaming R, Benjamin DJ, and Koellinger PD

Abstract

We conducted a genome-wide association study on income among individuals of European descent (N = 668,288) to investigate the relationship between socio-economic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes (the Income Factor). Our polygenic index captures 1-5% of income variance, with only one fourth due to direct genetic effects. A phenome-wide association study using this index showed reduced risks for diseases including hypertension, obesity, type 2 diabetes, depression, asthma and back pain. The Income Factor had a substantial genetic correlation (0.92, s.e. = 0.006) with educational attainment. Accounting for the genetic overlap of educational attainment with income revealed that the remaining genetic signal was linked to better mental health but reduced physical health and increased risky behaviours such as drinking and smoking. These findings highlight the complex genetic influences on income and health., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Published
2025
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41. Author Correction: Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction.

Author

Karlsson Linnér R, Mallard TT, Barr PB, Sanchez-Roige S, Madole JW, Driver MN, Poore HE, de Vlaming R, Grotzinger AD, Tielbeek JJ, Johnson EC, Liu M, Rosenthal SB, Ideker T, Zhou H, Kember RL, Pasman JA, Verweij KJH, Liu DJ, Vrieze S, Kranzler HR, Gelernter J, Harris KM, Tucker-Drob EM, Waldman ID, Palmer AA, Harden KP, Koellinger PD, and Dick DM

Published
2025
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42. Cohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G).

Author

Koellinger PD, Okbay A, Kweon H, Schweinert A, Linnér RK, Goebel J, Richter D, Reiber L, Zweck BM, Belsky DW, Biroli P, Mata R, Tucker-Drob EM, Harden KP, Wagner G, and Hertwig R

Subjects
Humans, Educational Status, Individuality, Germany epidemiology, Socioeconomic Factors, Genome-Wide Association Study, Academic Success
Abstract

The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of respondents living in private households in Germany. The dataset offers a valuable life course panorama, encompassing living conditions, socioeconomic status, familial connections, personality traits, values, preferences, health, and well-being. To amplify research opportunities further, we have extended the SOEP Innovation Sample (SOEP-IS) by collecting genetic data from 2,598 participants, yielding the first genotyped dataset for Germany based on a representative population sample (SOEP-G). The sample includes 107 full-sibling pairs, 501 parent-offspring pairs, and 152 triads, which overlap with the parent-offspring pairs. Leveraging the results from well-powered genome-wide association studies, we created a repository comprising 66 polygenic indices (PGIs) in the SOEP-G sample. We show that the PGIs for height, BMI, and educational attainment capture 22∼24%, 12∼13%, and 9% of the variance in the respective phenotypes. Using the PGIs for height and BMI, we demonstrate that the considerable increase in average height and the decrease in average BMI in more recent birth cohorts cannot be attributed to genetic shifts within the German population or to age effects alone. These findings suggest an important role of improved environmental conditions in driving these changes. Furthermore, we show that higher values in the PGIs for educational attainment and the highest math class are associated with better self-rated health, illustrating complex relationships between genetics, cognition, behavior, socio-economic status, and health. In summary, the SOEP-G data and the PGI repository we created provide a valuable resource for studying individual differences, inequalities, life-course development, health, and interactions between genetic predispositions and the environment., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Koellinger et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2023
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43. Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics.

Author

Williams CM, Poore H, Tanksley PT, Kweon H, Courchesne-Krak NS, Londono-Correa D, Mallard TT, Barr P, Koellinger PD, Waldman ID, Sanchez-Roige S, Harden KP, Palmer AA, Dick DM, and Karlsson Linnér R

Subjects
Phenotype, Genomics methods, Multifactorial Inheritance, Genome-Wide Association Study methods, Polymorphism, Single Nucleotide genetics
Abstract

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, although down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci; the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses were found robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who generate and share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers' use of the summary statistics., (© 2023. The Author(s).)

Published
2023
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44. Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities.

Author

Mallard TT, Linnér RK, Grotzinger AD, Sanchez-Roige S, Seidlitz J, Okbay A, de Vlaming R, Meddens SFW, Palmer AA, Davis LK, Tucker-Drob EM, Kendler KS, Keller MC, Koellinger PD, and Harden KP

Abstract

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of psychopathology versus rarer forms of serious mental illness. Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest that the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind rather than merely in degree., Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests.

Published
2022
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45. Human brain anatomy reflects separable genetic and environmental components of socioeconomic status.

Author

Kweon H, Aydogan G, Dagher A, Bzdok D, Ruff CC, Nave G, Farah MJ, and Koellinger PD

Abstract

Socioeconomic status (SES) correlates with brain structure, a relation of interest given the long-observed relations of SES to cognitive abilities and health. Yet, major questions remain open, in particular, the pattern of causality that underlies this relation. In an unprecedently large study, here, we assess genetic and environmental contributions to SES differences in neuroanatomy. We first establish robust SES-gray matter relations across a number of brain regions, cortical and subcortical. These regional correlates are parsed into predominantly genetic factors and those potentially due to the environment. We show that genetic effects are stronger in some areas (prefrontal cortex, insula) than others. In areas showing less genetic effect (cerebellum, lateral temporal), environmental factors are likely to be influential. Our results imply a complex interplay of genetic and environmental factors that influence the SES-brain relation and may eventually provide insights relevant to policy.

Published
2022
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46. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

Author

Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, Chittoor G, Ahlskog R, Lind PA, Palviainen T, van der Zee MD, Cheesman R, Mangino M, Wang Y, Li S, Klaric L, Ratliff SM, Bielak LF, Nygaard M, Giannelis A, Willoughby EA, Reynolds CA, Balbona JV, Andreassen OA, Ask H, Baras A, Bauer CR, Boomsma DI, Campbell A, Campbell H, Chen Z, Christofidou P, Corfield E, Dahm CC, Dokuru DR, Evans LM, de Geus EJC, Giddaluru S, Gordon SD, Harden KP, Hill WD, Hughes A, Kerr SM, Kim Y, Kweon H, Latvala A, Lawlor DA, Li L, Lin K, Magnus P, Magnusson PKE, Mallard TT, Martikainen P, Mills MC, Njølstad PR, Overton JD, Pedersen NL, Porteous DJ, Reid J, Silventoinen K, Southey MC, Stoltenberg C, Tucker-Drob EM, Wright MJ, Hewitt JK, Keller MC, Stallings MC, Lee JJ, Christensen K, Kardia SLR, Peyser PA, Smith JA, Wilson JF, Hopper JL, Hägg S, Spector TD, Pingault JB, Plomin R, Havdahl A, Bartels M, Martin NG, Oskarsson S, Justice AE, Millwood IY, Hveem K, Naess Ø, Willer CJ, Åsvold BO, Koellinger PD, Kaprio J, Medland SE, Walters RG, Benjamin DJ, Turley P, Evans DM, Davey Smith G, Hayward C, Brumpton B, Hemani G, and Davies NM

Subjects
Humans, Mendelian Randomization Analysis, Multifactorial Inheritance genetics, Phenotype, Genome-Wide Association Study, Polymorphism, Single Nucleotide genetics
Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects., (© 2022. The Author(s).)

Published
2022
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47. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals.

Author

Okbay A, Wu Y, Wang N, Jayashankar H, Bennett M, Nehzati SM, Sidorenko J, Kweon H, Goldman G, Gjorgjieva T, Jiang Y, Hicks B, Tian C, Hinds DA, Ahlskog R, Magnusson PKE, Oskarsson S, Hayward C, Campbell A, Porteous DJ, Freese J, Herd P, Watson C, Jala J, Conley D, Koellinger PD, Johannesson M, Laibson D, Meyer MN, Lee JJ, Kong A, Yengo L, Cesarini D, Turley P, Visscher PM, Beauchamp JP, Benjamin DJ, and Young AI

Subjects
Humans, Polymorphism, Single Nucleotide genetics, Genome-Wide Association Study, Multifactorial Inheritance genetics
Abstract

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57., (© 2022. The Author(s).)

Published
2022
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48. Associations between alcohol consumption and gray and white matter volumes in the UK Biobank.

Author

Daviet R, Aydogan G, Jagannathan K, Spilka N, Koellinger PD, Kranzler HR, Nave G, and Wetherill RR

Subjects
Aged, Alcohol Drinking, Biological Specimen Banks, Brain diagnostic imaging, Brain physiology, Gray Matter diagnostic imaging, Humans, Magnetic Resonance Imaging, Middle Aged, United Kingdom, White Matter diagnostic imaging
Abstract

Heavy alcohol consumption has been associated with brain atrophy, neuronal loss, and poorer white matter fiber integrity. However, there is conflicting evidence on whether light-to-moderate alcohol consumption shows similar negative associations with brain structure. To address this, we examine the associations between alcohol intake and brain structure using multimodal imaging data from 36,678 generally healthy middle-aged and older adults from the UK Biobank, controlling for numerous potential confounds. Consistent with prior literature, we find negative associations between alcohol intake and brain macrostructure and microstructure. Specifically, alcohol intake is negatively associated with global brain volume measures, regional gray matter volumes, and white matter microstructure. Here, we show that the negative associations between alcohol intake and brain macrostructure and microstructure are already apparent in individuals consuming an average of only one to two daily alcohol units, and become stronger as alcohol intake increases., (© 2022. The Author(s).)

Published
2022
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49. Genetic risk scores in life insurance underwriting.

Author

Karlsson Linnér R and Koellinger PD

Subjects
Genetic Testing, Humans, Retirement, Risk Factors, Insurance, Insurance, Life
Abstract

Genetic tests that predict the lifetime risk of common medical conditions are fast becoming more accurate and affordable. The life insurance industry is interested in using predictive genetic tests in the underwriting process, but more research is needed to establish whether this nascent form of genetic testing can refine the process over conventional underwriting factors. Here, we perform Cox regression of survival on a battery of genetic risk scores for common medical conditions and mortality risks in the Health and Retirement Study, without returning results to participants. Adjusted for covariates in a relevant insurance scenario, the scores could improve mortality risk classification by identifying 2.6 years shorter median lifespan in the highest decile of total genetic liability. We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Published
2022
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50. Resource profile and user guide of the Polygenic Index Repository.

Author

Becker J, Burik CAP, Goldman G, Wang N, Jayashankar H, Bennett M, Belsky DW, Karlsson Linnér R, Ahlskog R, Kleinman A, Hinds DA, Caspi A, Corcoran DL, Moffitt TE, Poulton R, Sugden K, Williams BS, Harris KM, Steptoe A, Ajnakina O, Milani L, Esko T, Iacono WG, McGue M, Magnusson PKE, Mallard TT, Harden KP, Tucker-Drob EM, Herd P, Freese J, Young A, Beauchamp JP, Koellinger PD, Oskarsson S, Johannesson M, Visscher PM, Meyer MN, Laibson D, Cesarini D, Benjamin DJ, Turley P, and Okbay A

Subjects
Data Analysis, Genome-Wide Association Study, Humans, Databases, Genetic, Multifactorial Inheritance, Polymorphism, Single Nucleotide
Abstract

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Published
2021
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