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1. 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

2. 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

4. Time preferences are reliable across time-horizons and verbal versus experiential tasks.

Author

Lukinova E, Wang Y, Lehrer SF, and Erlich JC

Subjects
Adolescent, Adult, Female, Healthy Volunteers, Humans, Male, Students, Young Adult, Delay Discounting, Psychological Tests
Abstract

Individual differences in delay-discounting correlate with important real world outcomes, for example education, income, drug use, and criminality. As such, delay-discounting has been extensively studied by economists, psychologists and neuroscientists to reveal its behavioral and biological mechanisms in both human and non-human animal models. However, two major methodological differences hinder comparing results across species. Human studies present long time-horizon options verbally, whereas animal studies employ experiential cues and short delays. To bridge these divides, we developed a novel language-free experiential task inspired by animal decision-making studies. We found that the ranks of subjects' time-preferences were reliable across both verbal/experiential and second/day differences. Yet, discount factors scaled dramatically across the tasks, indicating a strong effect of temporal context. Taken together, this indicates that individuals have a stable, but context-dependent, time-preference that can be reliably assessed using different methods, providing a foundation to bridge studies of time-preferences across species., Editorial Note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter)., Competing Interests: EL, YW, SL, JE No competing interests declared, (© 2019, Lukinova et al.)

Published
2019
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5. 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, Nguyen-Viet TA, Bowers P, Sidorenko J, Karlsson Linnér R, 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, Mägi R, Trampush JW, Verma SS, Wu Y, Lam M, Zhao JH, Zheng Z, Boardman JD, Campbell H, Freese J, Harris KM, Hayward C, Herd P, Kumari M, Lencz T, Luan J, 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 J, Johannesson M, Koellinger PD, Turley P, Visscher PM, Benjamin DJ, and Cesarini D

Subjects
Adult, Aged, Aged, 80 and over, Cohort Studies, Educational Status, Female, Genome-Wide Association Study methods, Humans, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Multifactorial Inheritance
Abstract

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Published
2018
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6. Genome-wide association study identifies 74 loci associated with educational attainment.

Author

Okbay A, 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 TS, Bacelis J, Baumbach C, Bjornsdottir G, Brandsma JH, Pina Concas M, 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 SJ, deLeeuw 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 D, 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, 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, Halldórsson BV, Harris TB, Heath AC, Hocking LJ, Holliday EG, Homuth G, Horan MA, Hottenga JJ, de Jager PL, Joshi PK, 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, Liewald DC, Loukola A, Madden PA, Mägi R, Mäki-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 WE, Palotie A, Paternoster L, Pedersen NL, Petrovic KE, Porteous DJ, Räikkönen K, Ring SM, Robino A, 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 S, Vaccargiu S, Quaye L, van Rooij FJ, Venturini C, Vinkhuyzen AA, Völker U, Völzke 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, Bültmann U, Chabris CF, Cucca F, Cusi D, Deary IJ, Dedoussis GV, van Duijn CM, Eriksson JG, Franke B, Franke L, Gasparini P, Gejman PV, Gieger C, Grabe HJ, Gratten J, Groenen PJ, Gudnason V, van der Harst P, Hayward C, Hinds DA, Hoffmann W, Hyppönen E, Iacono WG, Jacobsson B, Järvelin MR, Jöckel KH, Kaprio J, Kardia SL, Lehtimäki T, Lehrer SF, Magnusson PK, Martin NG, McGue M, Metspalu A, Pendleton N, Penninx BW, Perola M, Pirastu N, 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, Wilson JF, Wright AF, Conley DC, Krueger RF, Davey Smith G, Hofman A, Laibson DI, Medland SE, Meyer MN, Yang J, Johannesson M, Visscher PM, Esko T, Koellinger PD, Cesarini D, and Benjamin DJ

Subjects
Alzheimer Disease genetics, Bipolar Disorder genetics, Cognition, Computational Biology, Gene-Environment Interaction, Humans, Molecular Sequence Annotation, Schizophrenia genetics, United Kingdom, Brain metabolism, Educational Status, Fetus metabolism, Gene Expression Regulation genetics, Genome-Wide Association Study, Polymorphism, Single Nucleotide genetics
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. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample 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
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7. Cohort of birth modifies the association between FTO genotype and BMI.

Author

Rosenquist JN, Lehrer SF, O'Malley AJ, Zaslavsky AM, Smoller JW, and Christakis NA

Subjects
Adult, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Cohort Studies, Female, Gene Frequency, Gene-Environment Interaction, Genetic Variation, Genotype, Humans, Linear Models, Male, Middle Aged, Models, Genetic, Obesity genetics, Obesity pathology, United States epidemiology, Body Mass Index, Obesity epidemiology, Proteins genetics
Abstract

A substantial body of research has explored the relative roles of genetic and environmental factors on phenotype expression in humans. Recent research has also sought to identify gene-environment (or g-by-e) interactions, with mixed success. One potential reason for these mixed results may relate to the fact that genetic effects might be modified by changes in the environment over time. For example, the noted rise of obesity in the United States in the latter part of the 20th century might reflect an interaction between genetic variation and changing environmental conditions that together affect the penetrance of genetic influences. To evaluate this hypothesis, we use longitudinal data from the Framingham Heart Study collected over 30 y from a geographically relatively localized sample to test whether the well-documented association between the rs993609 variant of the FTO (fat mass and obesity associated) gene and body mass index (BMI) varies across birth cohorts, time period, and the lifecycle. Such cohort and period effects integrate many potential environmental factors, and this gene-by-environment analysis examines interactions with both time-varying contemporaneous and historical environmental influences. Using constrained linear age-period-cohort models that include family controls, we find that there is a robust relationship between birth cohort and the genotype-phenotype correlation between the FTO risk allele and BMI, with an observed inflection point for those born after 1942. These results suggest genetic influences on complex traits like obesity can vary over time, presumably because of global environmental changes that modify allelic penetrance.

Published
2015
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8. A genetic instrumental variables analysis of the effects of prenatal smoking on birth weight: evidence from two samples.

Author

Wehby GL, Fletcher JM, Lehrer SF, Moreno LM, Murray JC, Wilcox A, and Lie RT

Subjects
Adolescent, Female, Genetic Markers, Genetic Predisposition to Disease epidemiology, Humans, Infant, Least-Squares Analysis, Male, Maternal Behavior, Norway epidemiology, Pregnancy, Smoking epidemiology, United States epidemiology, Birth Weight, Prenatal Exposure Delayed Effects epidemiology, Smoking adverse effects, Smoking genetics
Abstract

There is a large literature showing the detrimental effects of prenatal smoking on birth and childhood health outcomes. It is somewhat unclear though, whether these effects are causal or reflect other characteristics and choices by mothers who choose to smoke that may also affect child health outcomes or biased reporting of smoking. In this paper we use genetic markers that predict smoking behaviors as instruments to address the endogeneity of smoking choices in the production of birth and childhood health outcomes. Our results indicate that prenatal smoking produces more dramatic declines in birth weight than estimates that ignore the endogeneity of prenatal smoking, which is consistent with previous studies with non-genetic instruments. We use data from two distinct samples from Norway and the United States with different measured instruments and find nearly identical results. The study provides a novel application that can be extended to study several behavioral impacts on health and social and economic outcomes.

Published
2011
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9. The impact of poor health on academic performance: New evidence using genetic markers.

Author

Ding W, Lehrer SF, Rosenquist JN, and Audrain-McGovern J

Subjects
Adolescent, Attention Deficit Disorder with Hyperactivity genetics, Comorbidity, Evidence-Based Medicine, Female, Humans, Male, Mental Disorders genetics, Mental Health, Obesity genetics, Virginia, Educational Measurement, Genetic Markers, Health Status
Abstract

This paper examines the influence of health conditions on academic performance during adolescence. To account for the endogeneity of health outcomes and their interactions with risky behaviors we exploit natural variation within a set of genetic markers across individuals. We present evidence that specific genetic markers have good statistical properties to identify the impacts of ADHD, depression and obesity. These markers help reveal a new dynamism from poor health to lower academic achievement with substantial heterogeneity in their impacts across genders. Our investigation further exposes the considerable challenges in identifying health impacts due to the prevalence of comorbid health conditions, with clear implications for the health economics literature.

Published
2009
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10. Worker sorting, compensating differentials and health insurance: evidence from displaced workers.

Author

Lehrer SF and Pereira NS

Subjects
Adult, Developed Countries, Female, Humans, Male, Middle Aged, Models, Econometric, Salaries and Fringe Benefits statistics & numerical data, Evidence-Based Medicine, Health Benefit Plans, Employee economics, Salaries and Fringe Benefits economics
Abstract

This article introduces an empirical strategy to the compensating differentials literature that (i) allows both individual observed and unobserved characteristics to be rewarded differently in firms based on health insurance provision, and (ii) selection to jobs that provide benefits to operate on both sides of the labor market. Estimates of this model are used to directly test empirical assumptions that are made with popular econometric strategies in the health economics literature. Our estimates reject the assumptions underlying numerous cross sectional and longitudinal estimators. We find that the provision of health insurance has influenced wage inequality. Finally, our results suggest there have been substantial changes in how displaced workers sort to firms that offer health insurance benefits over the past two decades. We discuss the implications of our findings for the compensating differentials literature.

Published
2007
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