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Mbarek H, Gordon SD, Duffy DL, Hubers N, Mortlock S, Beck JJ, Hottenga JJ, Pool R, Dolan CV, Actkins KV, Gerring ZF, Van Dongen J, Ehli EA, Iacono WG, Mcgue M, Chasman DI, Gallagher CS, Schilit SLP, Morton CC, Paré G, Willemsen G, Whiteman DC, Olsen CM, Derom C, Vlietinck R, Gudbjartsson D, Cannon-Albright L, Krapohl E, Plomin R, Magnusson PKE, Pedersen NL, Hysi P, Mangino M, Spector TD, Palviainen T, Milaneschi Y, Penninnx BW, Campos AI, Ong KK, Perry JRB, Lambalk CB, Kaprio J, Ólafsson Í, Duroure K, Revenu C, Rentería ME, Yengo L, Davis L, Derks EM, Medland SE, Stefansson H, Stefansson K, Del Bene F, Reversade B, Montgomery GW, Boomsma DI, Martin NG. Genome-wide association study meta-analysis of dizygotic twinning illuminates genetic regulation of female fecundity. Hum Reprod 2024; 39:240-257. [PMID: 38052102 PMCID: PMC10767824 DOI: 10.1093/humrep/dead247] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/14/2023] [Indexed: 12/07/2023] Open
Abstract
STUDY QUESTION Which genetic factors regulate female propensity for giving birth to spontaneous dizygotic (DZ) twins? SUMMARY ANSWER We identified four new loci, GNRH1, FSHR, ZFPM1, and IPO8, in addition to previously identified loci, FSHB and SMAD3. WHAT IS KNOWN ALREADY The propensity to give birth to DZ twins runs in families. Earlier, we reported that FSHB and SMAD3 as associated with DZ twinning and female fertility measures. STUDY DESIGN, SIZE, DURATION We conducted a genome-wide association meta-analysis (GWAMA) of mothers of spontaneous dizygotic (DZ) twins (8265 cases, 264 567 controls) and of independent DZ twin offspring (26 252 cases, 417 433 controls). PARTICIPANTS/MATERIALS, SETTING, METHODS Over 700 000 mothers of DZ twins, twin individuals and singletons from large cohorts in Australia/New Zealand, Europe, and the USA were carefully screened to exclude twins born after use of ARTs. Genetic association analyses by cohort were followed by meta-analysis, phenome wide association studies (PheWAS), in silico and in vivo annotations, and Zebrafish functional validation. MAIN RESULTS AND THE ROLE OF CHANCE This study enlarges the sample size considerably from previous efforts, finding four genome-wide significant loci, including two novel signals and a further two novel genes that are implicated by gene level enrichment analyses. The novel loci, GNRH1 and FSHR, have well-established roles in female reproduction whereas ZFPM1 and IPO8 have not previously been implicated in female fertility. We found significant genetic correlations with multiple aspects of female reproduction and body size as well as evidence for significant selection against DZ twinning during human evolution. The 26 top single nucleotide polymorphisms (SNPs) from our GWAMA in European-origin participants weakly predicted the crude twinning rates in 47 non-European populations (r = 0.23 between risk score and population prevalence, s.e. 0.11, 1-tail P = 0.058) indicating that genome-wide association studies (GWAS) are needed in African and Asian populations to explore the causes of their respectively high and low DZ twinning rates. In vivo functional tests in zebrafish for IPO8 validated its essential role in female, but not male, fertility. In most regions, risk SNPs linked to known expression quantitative trait loci (eQTLs). Top SNPs were associated with in vivo reproductive hormone levels with the top pathways including hormone ligand binding receptors and the ovulation cycle. LARGE SCALE DATA The full DZT GWAS summary statistics will made available after publication through the GWAS catalog (https://www.ebi.ac.uk/gwas/). LIMITATIONS, REASONS FOR CAUTION Our study only included European ancestry cohorts. Inclusion of data from Africa (with the highest twining rate) and Asia (with the lowest rate) would illuminate further the biology of twinning and female fertility. WIDER IMPLICATIONS OF THE FINDINGS About one in 40 babies born in the world is a twin and there is much speculation on why twinning runs in families. We hope our results will inform investigations of ovarian response in new and existing ARTs and the causes of female infertility. STUDY FUNDING/COMPETING INTEREST(S) Support for the Netherlands Twin Register came from the Netherlands Organization for Scientific Research (NWO) and The Netherlands Organization for Health Research and Development (ZonMW) grants, 904-61-193, 480-04-004, 400-05-717, Addiction-31160008, 911-09-032, Biobanking and Biomolecular Resources Research Infrastructure (BBMRI.NL, 184.021.007), Royal Netherlands Academy of Science Professor Award (PAH/6635) to DIB, European Research Council (ERC-230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH R01 HD042157-01A1) and the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health and Grand Opportunity grants 1RC2 MH089951. The QIMR Berghofer Medical Research Institute (QIMR) study was supported by grants from the National Health and Medical Research Council (NHMRC) of Australia (241944, 339462, 389927, 389875, 389891, 389892, 389938, 443036, 442915, 442981, 496610, 496739, 552485, 552498, 1050208, 1075175). L.Y. is funded by Australian Research Council (Grant number DE200100425). The Minnesota Center for Twin and Family Research (MCTFR) was supported in part by USPHS Grants from the National Institute on Alcohol Abuse and Alcoholism (AA09367 and AA11886) and the National Institute on Drug Abuse (DA05147, DA13240, and DA024417). The Women's Genome Health Study (WGHS) was funded by the National Heart, Lung, and Blood Institute (HL043851 and HL080467) and the National Cancer Institute (CA047988 and UM1CA182913), with support for genotyping provided by Amgen. Data collection in the Finnish Twin Registry has been supported by the Wellcome Trust Sanger Institute, the Broad Institute, ENGAGE-European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413, National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145, AA-09203, AA15416, and K02AA018755) and the Academy of Finland (grants 100499, 205585, 118555, 141054, 264146, 308248, 312073 and 336823 to J. Kaprio). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. For NESDA, funding was obtained from the Netherlands Organization for Scientific Research (Geestkracht program grant 10000-1002), the Center for Medical Systems Biology (CSMB, NVVO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University's Institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH, ROI D0042157-01A, MH081802, Grand Opportunity grants 1 RC2 Ml-1089951 and IRC2 MH089995). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by BiG Grid, the Dutch e-Science Grid, which is financially supported by NWO. Work in the Del Bene lab was supported by the Programme Investissements d'Avenir IHU FOReSIGHT (ANR-18-IAHU-01). C.R. was supported by an EU Horizon 2020 Marie Skłodowska-Curie Action fellowship (H2020-MSCA-IF-2014 #661527). H.S. and K.S. are employees of deCODE Genetics/Amgen. The other authors declare no competing financial interests. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Hamdi Mbarek
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Qatar Genome Program, Qatar Foundation, Doha, Qatar
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - David L Duffy
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Nikki Hubers
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Sally Mortlock
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Jeffrey J Beck
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD, USA
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
| | - Conor V Dolan
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
| | - Ky’Era V Actkins
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | | | - Jenny Van Dongen
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Erik A Ehli
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Matt Mcgue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Daniel I Chasman
- Harvard Medical School, Harvard University, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | | | - Samantha L P Schilit
- Harvard Medical School, Harvard University, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Cynthia C Morton
- Harvard Medical School, Harvard University, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Guillaume Paré
- Population Health Research Institute, McMaster University, Hamilton, ON, Canada
| | - Gonneke Willemsen
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | | | | | | | | | - Eva Krapohl
- Medical Research Council Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Statistical Sciences & Innovation, UCB Biosciences GmbH, Monheim, Germany
| | - Robert Plomin
- Medical Research Council Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Pirro Hysi
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, UK
| | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, UK
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
| | - Timothy D Spector
- Department of Twin Research & Genetic Epidemiology, King’s College London, London, UK
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Yuri Milaneschi
- Department of Psychiatry, EMGO Institute for Health and Care Research, Vrije Universiteit, Amsterdam, The Netherlands
| | - Brenda W Penninnx
- Department of Psychiatry, EMGO Institute for Health and Care Research, Vrije Universiteit, Amsterdam, The Netherlands
| | - Adrian I Campos
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Ken K Ong
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Cornelis B Lambalk
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
- Amsterdam University Medical Centers Location VU Medical Center, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Ísleifur Ólafsson
- Department of Clinical Biochemistry, National University Hospital of Iceland, Reykjavik, Iceland
| | - Karine Duroure
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Céline Revenu
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | | | - Loic Yengo
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Lea Davis
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Eske M Derks
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | | | - Filippo Del Bene
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Bruno Reversade
- Genome Institute of Singapore, Laboratory of Human Genetics & Therapeutics, A*STAR, Singapore, Singapore
- Smart-Health Initiative, BESE, KAUST, Thuwal, Saudi Arabia
| | - Grant W Montgomery
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
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Ross JM, Karoly HC, Zellers SM, Ellingson JM, Corley RP, Iacono WG, Hewitt JK, McGue M, Vrieze S, Hopfer CJ. Evaluating substance use outcomes of recreational cannabis legalization using a unique co-twin control design. THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 2023; 49:630-639. [PMID: 37262386 PMCID: PMC10689567 DOI: 10.1080/00952990.2022.2163177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/28/2022] [Accepted: 12/17/2022] [Indexed: 06/03/2023]
Abstract
Background: As more states pass recreational cannabis legalization (RCL), we must understand how RCL affects substance use.Objectives: The current study aims to examine the effect of RCL on lifetime and past-year use of cannabis, alcohol, tobacco, and other drugs, frequency of cannabis, alcohol, and tobacco use, co-use of cannabis with alcohol and tobacco, and consequences from cannabis and alcohol use.Methods: We used a unique, co-twin control design of twin pairs who were discordant for living in a state with RCL between 2018 and 2021. The sample consisted of 3,830 adult twins (41% male), including 232 twin pairs discordant for RCL. Problems from alcohol and cannabis use were assessed via the Brief Marijuana Consequences Questionnaire and the Brief Young Adult Alcohol Consequences Questionnaire.Results: Results indicated that the twin living in an RCL state was more likely to endorse past-year cannabis use (OR = 1.56, p = .009), greater number of cannabis use days in the past 6 months (β = 0.47, p = .019), but not more negative consequences from cannabis use (β = 0.21, p = .456) compared to their co-twin in a non-RCL state. There were no differences within-twin pairs in frequency of alcohol use (β=-0.05, p = .601), but the RCL twin reported fewer negative consequences from alcohol use (β=-0.29, p = .016) compared to their co-twin in a non-RCL state. We did not observe any other differences within-twin pairs on other outcomes.Conclusion: These results suggest that living in an RCL state is associated with greater cannabis frequency but not more negative consequences from cannabis use than living in a non-RCL state.
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Affiliation(s)
- J. Megan Ross
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Hollis C. Karoly
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
| | | | - Jarrod M. Ellingson
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Robin P. Corley
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - William G. Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - John K. Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Christian J. Hopfer
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
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Paulich KN, Freis SM, Dokuru DR, Alexander JD, Vrieze SI, Corley RP, McGue M, Hewitt JK, Stallings MC. Exploring Relationships Between Internalizing Problems and Risky Sexual Behavior: A Twin Study. Behav Genet 2023; 53:331-347. [PMID: 37165251 PMCID: PMC11138211 DOI: 10.1007/s10519-023-10146-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/25/2023] [Indexed: 05/12/2023]
Abstract
Previous research links risky sexual behavior (RSB) to externalizing problems and to substance use, but little research has been conducted on relationships between internalizing problems (INT) and RSB. The current study addresses that literature gap, using both a twin sample from Colorado (N = 2567) and a second twin sample from Minnesota (N = 1131) in attempt to replicate initial results. We explored the hypothesis that the latent variable INT would be more strongly associated with the latent variable RSB for females than for males, examining relationships between INT and RSB via phenotypic confirmatory factor analysis and multivariate twin analyses. We found a small but significant phenotypic association between the latent variables. However, despite using two large twin samples, limited power restricted our ability to identify the genetic and environmental mechanisms underlying this association. Our sex differences hypothesis was not fully supported in either sample and requires further investigation. Our findings illustrate the complexity of the relationship between internalizing problems and risky sexual behavior.
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Affiliation(s)
- Katie N Paulich
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th St, Boulder, CO, 80303, USA.
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.
| | - Samantha M Freis
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th St, Boulder, CO, 80303, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Deepika R Dokuru
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th St, Boulder, CO, 80303, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | | | - Scott I Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Robin P Corley
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th St, Boulder, CO, 80303, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th St, Boulder, CO, 80303, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Michael C Stallings
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th St, Boulder, CO, 80303, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
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Majumdar S, Basu S, McGue M, Chatterjee S. Simultaneous selection of multiple important single nucleotide polymorphisms in familial genome wide association studies data. Sci Rep 2023; 13:8476. [PMID: 37231056 PMCID: PMC10213008 DOI: 10.1038/s41598-023-35379-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 05/17/2023] [Indexed: 05/27/2023] Open
Abstract
We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as 'single SNP association analysis'. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach-based on the e-values framework-for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption.
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Affiliation(s)
- Subhabrata Majumdar
- University of Minnesota Twin Cities, Minneapolis, USA.
- AI Risk and Vulnerability Alliance, Seattle, USA.
| | - Saonli Basu
- University of Minnesota Twin Cities, Minneapolis, USA
| | - Matt McGue
- University of Minnesota Twin Cities, Minneapolis, USA
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Kong YF, Li SZ, Wang KW, Zhu B, Yuan YX, Li MK, Zhou JY. An Efficient Bayesian Method for Estimating the Degree of the Skewness of X Chromosome Inactivation Based on the Mixture of General Pedigrees and Unrelated Females. Biomolecules 2023; 13:biom13030543. [PMID: 36979477 PMCID: PMC10046098 DOI: 10.3390/biom13030543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Skewed X chromosome inactivation (XCI-S) has been reported to be associated with some X-linked diseases. Several methods have been proposed to estimate the degree of XCI-S (denoted as γ) for quantitative and qualitative traits based on unrelated females. However, there is no method available for estimating γ based on general pedigrees. Therefore, in this paper, we propose a Bayesian method to obtain the point estimate and the credible interval of γ based on the mixture of general pedigrees and unrelated females (called mixed data for brevity), which is also suitable for only general pedigrees. We consider the truncated normal prior and the uniform prior for γ. Further, we apply the eigenvalue decomposition and Cholesky decomposition to our proposed methods to accelerate the computation speed. We conduct extensive simulation studies to compare the performances of our proposed methods and two existing Bayesian methods which are only applicable to unrelated females. The simulation results show that the incorporation of general pedigrees can improve the efficiency of the point estimation and the precision and the accuracy of the interval estimation of γ. Finally, we apply the proposed methods to the Minnesota Center for Twin and Family Research data for their practical use.
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Affiliation(s)
- Yi-Fan Kong
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Shi-Zhu Li
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Kai-Wen Wang
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Bin Zhu
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Yu-Xin Yuan
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Meng-Kai Li
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Ji-Yuan Zhou
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
- Correspondence:
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A Century of Behavioral Genetics at the University of Minnesota. Twin Res Hum Genet 2022; 25:211-225. [PMID: 36734056 DOI: 10.1017/thg.2023.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The University of Minnesota has played an important role in the resurgence and eventual mainstreaming of human behavioral genetics in psychology and psychiatry. We describe this history in the context of three major movements in behavioral genetics: (1) radical eugenics in the early 20th century, (2) resurgence of human behavioral genetics in the 1960s, largely using twin and adoption designs to obtain more precise estimates of genetic and environmental influences on individual differences in behavior; and (3) use of measured genotypes to understand behavior. University of Minnesota scientists made significant contributions especially in (2) and (3) in the domains of cognitive ability, drug abuse and mental health, and endophenotypes. These contributions are illustrated through a historical perspective of major figures and events in behavioral genetics.
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Tielbeek JJ, Uffelmann E, Williams BS, Colodro-Conde L, Gagnon É, Mallard TT, Levitt BE, Jansen PR, Johansson A, Sallis HM, Pistis G, Saunders GRB, Allegrini AG, Rimfeld K, Konte B, Klein M, Hartmann AM, Salvatore JE, Nolte IM, Demontis D, Malmberg ALK, Burt SA, Savage JE, Sugden K, Poulton R, Harris KM, Vrieze S, McGue M, Iacono WG, Mota NR, Mill J, Viana JF, Mitchell BL, Morosoli JJ, Andlauer TFM, Ouellet-Morin I, Tremblay RE, Côté SM, Gouin JP, Brendgen MR, Dionne G, Vitaro F, Lupton MK, Martin NG, Castelao E, Räikkönen K, Eriksson JG, Lahti J, Hartman CA, Oldehinkel AJ, Snieder H, Liu H, Preisig M, Whipp A, Vuoksimaa E, Lu Y, Jern P, Rujescu D, Giegling I, Palviainen T, Kaprio J, Harden KP, Munafò MR, Morneau-Vaillancourt G, Plomin R, Viding E, Boutwell BB, Aliev F, Dick DM, Popma A, Faraone SV, Børglum AD, Medland SE, Franke B, Boivin M, Pingault JB, Glennon JC, Barnes JC, Fisher SE, Moffitt TE, Caspi A, Polderman TJC, Posthuma D. Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis. Mol Psychiatry 2022; 27:4453-4463. [PMID: 36284158 PMCID: PMC10902879 DOI: 10.1038/s41380-022-01793-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 08/03/2022] [Accepted: 09/09/2022] [Indexed: 01/14/2023]
Abstract
Despite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 28 discovery samples (N = 85,359) and five independent replication samples (N = 8058) with genotypic data and broad measures of ASB. We identified the first significant genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, p = 6.32 × 10-10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ strain) from controls (BALB/cByJ strain). Polygenic risk score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development. We found substantial genetic correlations of ASB with mental health (depression rg = 0.63, insomnia rg = 0.47), physical health (overweight rg = 0.19, waist-to-hip ratio rg = 0.32), smoking (rg = 0.54), cognitive ability (intelligence rg = -0.40), educational attainment (years of schooling rg = -0.46) and reproductive traits (age at first birth rg = -0.58, father's age at death rg = -0.54). Our findings provide a starting point toward identifying critical biosocial risk mechanisms for the development of ASB.
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Affiliation(s)
- Jorim J Tielbeek
- Center for Neurogenomics and Cognitive Research, Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands.
| | - Emil Uffelmann
- Center for Neurogenomics and Cognitive Research, Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - Benjamin S Williams
- Department of Psychology and Neuroscience, Trinity College of Arts and Sciences, Duke University, 2020 West Main Street, Durham, NC, 27705, USA
| | - Lucía Colodro-Conde
- Psychiatric Genetics, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Brisbane, QLD, 4006, Australia
| | - Éloi Gagnon
- Research Unit on Children's Psychosocial Maladjustment, École de psychologie, Université Laval, 2523 Allée des Bibliothèques, Quebec City, QC, G1V 0A6, Canada
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Brandt E Levitt
- Carolina Population Center, University of North Carolina at Chapel Hill, 123 Franklin St, Chapel Hill, NC, 27516, USA
| | - Philip R Jansen
- Center for Neurogenomics and Cognitive Research, Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - Ada Johansson
- Department of Psychology, Faculty of Arts, Psychology, and Theology, Åbo Akademi University, Tuomiokirkontori 3, FI-20500, Turku, Finland
| | - Hannah M Sallis
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield Road, Bristol, BS8 2BN, UK
| | - Giorgio Pistis
- Center for Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Route de Cery 25, CH-1008, Prilly, Vaud, Switzerland
| | - Gretchen R B Saunders
- Department of Psychology, University of Minnesota, 75 E. River Road, Minneapolis, MN, 55455, USA
| | - Andrea G Allegrini
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, DeCrespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Kaili Rimfeld
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, DeCrespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Bettina Konte
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Marieke Klein
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Groteplein 10, 6500 HB, Nijmegen, The Netherlands
| | - Annette M Hartmann
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Jessica E Salvatore
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands
| | - Ditte Demontis
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, 8000, Aarhus C, Aarhus, Denmark
| | - Anni L K Malmberg
- Department of Psychology and Logopedics, University of Helsinki, Haartmaninkatu 3, 00014, Helsinki, Finland
| | | | - Jeanne E Savage
- Center for Neurogenomics and Cognitive Research, Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - Karen Sugden
- Department of Psychology and Neuroscience, Trinity College of Arts and Sciences, Duke University, 2020 West Main Street, Durham, NC, 27705, USA
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, Dunedin, New Zealand
| | - Kathleen Mullan Harris
- Department of Sociology, University of North Carolina at Chapel Hill, CB# 3210, 201 Hamilton Hall, Chapel Hill, NC, 27599, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, 75 E. River Road, Minneapolis, MN, 55455, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, 75 E. River Road, Minneapolis, MN, 55455, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota, 75 E. River Road, Minneapolis, MN, 55455, USA
| | - Nina Roth Mota
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Geert Groteplein 10, 6500 HB, Nijmegen, The Netherlands
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Joana F Viana
- The Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, UK
| | - Brittany L Mitchell
- Genetic Epidemiology, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Brisbane, QLD, 4006, Australia
| | - Jose J Morosoli
- Psychiatric Genetics, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Brisbane, QLD, 4006, Australia
| | - Till F M Andlauer
- Department of Neurology, Technical University of Munich, 22 Ismaninger St., 81675, Munich, Germany
| | - Isabelle Ouellet-Morin
- Research Unit on Children's Psychosocial Maladjustment, École de criminologie, Université of Montreal, 3150 Rue Jean-Brillant, Montreal, QC, H3T 1N8, Canada
| | - Richard E Tremblay
- Research Unit on Children's Psychosocial Maladjustment, Département de pédiatrie et de psychologie, University of Montreal, 90 Avenue Vincent d'Indy, Montreal, QC, H2V 2S9, Canada
| | - Sylvana M Côté
- Research Unit on Children's Psychosocial Maladjustment, CHU Ste-Justine Research Center and Department of Social and Preventive Medicine, University of Montreal, 3175 Chemin de la Côte Ste-Catherine, Montreal, QC, H3T 1C5, Canada
| | - Jean-Philippe Gouin
- Department of Psychology, Concordia University, 7141 Sherbrooke St. West, Montreal, QC, H4B 1R6, Canada
| | - Mara R Brendgen
- Research Unit on Children's Psychosocial Maladjustment, Département de psychologie, Université du Québec à Montréal, CP 8888 succursale Centre-ville, Montreal, QC, H3C 3P8, Canada
| | - Ginette Dionne
- Research Unit on Children's Psychosocial Maladjustment, École de psychologie, Université Laval, 2523 Allée des Bibliothèques, Quebec City, QC, G1V 0A6, Canada
| | - Frank Vitaro
- Research Unit on Children's Psychosocial Maladjustment, CHU Sainte-Justine Research Center and University of Montreal, 3175 Chemin de la Côte Ste-Catherine, Montreal, QC, H3T 1C5, Canada
| | - Michelle K Lupton
- Genetic Epidemiology, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Brisbane, QLD, 4006, Australia
| | - Nicholas G Martin
- Genetic Epidemiology, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Brisbane, QLD, 4006, Australia
| | - Enrique Castelao
- Center for Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Route de Cery 25, CH-1008, Prilly, Vaud, Switzerland
| | - Katri Räikkönen
- Department of Psychology and Logopedics, University of Helsinki, Haartmaninkatu 3, 00014, Helsinki, Finland
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Tukholmankatu 8 B, Helsinki, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Haartmaninkatu 3, 00014, Helsinki, Finland
| | - Catharina A Hartman
- Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands
| | - Albertine J Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB, Groningen, The Netherlands
| | - Hexuan Liu
- School of Criminal Justice, University of Cincinnati, 2840 Bearcat Way, Cincinnati, OH, 45221, USA
| | - Martin Preisig
- Center for Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Route de Cery 25, CH-1008, Prilly, Vaud, Switzerland
| | - Alyce Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, PO Box 4, (Yliopistonkatu 3), 00014, Helsinki, Finland
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, PO Box 4, (Yliopistonkatu 3), 00014, Helsinki, Finland
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Patrick Jern
- Department of Psychology, Faculty of Arts, Psychology, and Theology, Åbo Akademi University, Tuomiokirkontori 3, FI-20500, Turku, Finland
| | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Ina Giegling
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, PO Box 4, (Yliopistonkatu 3), 00014, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, PO Box 4, (Yliopistonkatu 3), 00014, Helsinki, Finland
| | - Kathryn Paige Harden
- Department of Psychology and Population Research Center, University of Texas at Austin, 108 E Dean Keeton Stop #A8000, Austin, TX, 78712, USA
| | - Marcus R Munafò
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield Road, Bristol, BS8 2BN, UK
| | - Geneviève Morneau-Vaillancourt
- Research Unit on Children's Psychosocial Maladjustment, École de psychologie, Université Laval, 2523 Allée des Bibliothèques, Quebec City, QC, G1V 0A6, Canada
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, DeCrespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Essi Viding
- Division of Psychology and Language Sciences, University College London, London, UK
| | - Brian B Boutwell
- School of Applied Sciences, University of Mississippi, John D. Bower School of Population Health, University of Mississippi Medical Center, 84 Dormitory Row West, University, MS, 38677, USA
| | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University, Box 842018, 806W Franklin St, Richmond, VA, 23284, USA
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Box 842018, 806W Franklin St, Richmond, VA, 23284, USA
| | - Arne Popma
- Amsterdam UMC, VKC Psyche, Child and Adolescent Psychiatry & Psychosocial Care, Amsterdam, The Netherlands
| | - Stephen V Faraone
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Anders D Børglum
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, 8000, Aarhus C, Aarhus, Denmark
| | - Sarah E Medland
- Psychiatric Genetics, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Brisbane, QLD, 4006, Australia
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaivour, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Michel Boivin
- Research Unit on Children's Psychosocial Maladjustment, École de psychologie, Université Laval, 2523 Allée des Bibliothèques, Quebec City, QC, G1V 0A6, Canada
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Jeffrey C Glennon
- Conway Institute of Biomolecular and Biomedical Sciences, School of Medicine, University College Dublin, Dublin, Ireland
| | - J C Barnes
- School of Criminal Justice, University of Cincinnati, 2840 Bearcat Way, Cincinnati, OH, 45221, USA
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD, Nijmegen, The Netherlands
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Trinity College of Arts and Sciences, Duke University, 2020 West Main Street, Durham, NC, 27705, USA
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Trinity College of Arts and Sciences, Duke University, 2020 West Main Street, Durham, NC, 27705, USA
| | - Tinca J C Polderman
- Amsterdam UMC, VKC Psyche, Child and Adolescent Psychiatry & Psychosocial Care, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Center for Neurogenomics and Cognitive Research, Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
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8
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Saunders GRB, McGue M, Iacono WG, Vrieze S. Longitudinal effects and environmental moderation of ALDH2 and ADH1B gene variants on substance use from age 14 to 40. Dev Psychopathol 2022; 34:1-9. [PMID: 36102130 PMCID: PMC10011021 DOI: 10.1017/s0954579422000712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Alcohol use and dependence are strongly affected by variation in aldehyde dehydrogenase (ALDH2) and, to a lesser extent, alcohol dehydrogenase (ADH1B) genes. We use this genetic variation with an adoption design to test the causal role of alcohol use on other drug use, as well as the moderating role of adoptive parent, sibling, and peer alcohol use. Longitudinal models were run on 412 genotyped adopted individuals of East Asian ancestry with multiple assessments between ages 14 and 40. We found robust associations between alcohol frequency, quantity, and maximum drinks and ALDH2, but not ADH1B, status. The magnitude of the ALDH2 protective effect increased with age, particularly for maximum drinks, though estimates were smaller than previously reported in ancestrally similar individuals in East/North-East Asian countries. These results suggest that sociocultural factors in Minnesota may reduce the protective effects of ALDH2. We found that peer alcohol use, but not parent or sibling use, predicted adopted offspring's use, and that these environmental influences did not vary by ALDH2 status. Finally, we did not find strong evidence of associations between ALDH2 status and tobacco, marijuana, or illegal drug use, contrary to expectation if alcohol serves as a gateway to use of other drugs.
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Affiliation(s)
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN55455, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN55455, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN55455, USA
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9
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Not by g alone: The benefits of a college education among individuals with low levels of general cognitive ability. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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10
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Hupalo D, Forsberg CW, Goldberg J, Kremen WS, Lyons MJ, Soltis AR, Viollet C, Ursano RJ, Stein MB, Franz CE, Sun YV, Vaccarino V, Smith NL, Dalgard CL, Wilkerson MD, Pollard HB. Rare variant association study of veteran twin whole-genomes links severe depression with a nonsynonymous change in the neuronal gene BHLHE22. World J Biol Psychiatry 2022; 23:295-306. [PMID: 34664540 PMCID: PMC9148382 DOI: 10.1080/15622975.2021.1980316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
OBJECTIVES Major Depressive Disorder (MDD) is a complex neuropsychiatric disease with known genetic associations, but without known links to rare variation in the human genome. Here we aim to identify rare genetic variants associated with MDD using deep whole-genome sequencing data in an independent population. METHODS We report the sequencing of 1,688 whole genomes in a large sample of male-male Veteran twins. Depression status was classified based on a structured diagnostic interview according to DSM-III-R diagnostic criteria. Searching only rare variants in genomic regions from recent GWAS on MDD, we used the optimised sequence kernel association test and Fisher's Exact test to fine map loci associated with severe depression. RESULTS Our analysis identified one gene associated with severe depression, basic helix loop helix e22 (PAdjusted = 0.03) via SKAT-O test between unrelated severely depressed cases compared to unrelated non-depressed controls. The same gene BHLHE22 had a non-silent variant rs13279074 (PAdjusted = 0.032) based on a single variant Fisher's Exact test between unrelated severely depressed cases compared to unrelated non-depressed controls. CONCLUSION The gene BHLHE22 shows compelling genetic evidence of directly impacting the severe depression phenotype. Together these results advance understanding of the genetic contribution to major depressive disorder in a new cohort and link a rare variant to severe forms of the disorder.
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Affiliation(s)
- Daniel Hupalo
- The American Genome Center, Collaborative Health Initiative Research Program, and Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA
| | - Christopher W. Forsberg
- Seattle Epidemiologic Research and Information Center, Office of Research and Development, U.S. Department of Veteran Affairs, Seattle, WA, USA
| | - Jack Goldberg
- Seattle Epidemiologic Research and Information Center, Office of Research and Development, U.S. Department of Veteran Affairs, Seattle, WA, USA;,Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - William S. Kremen
- Department of Psychiatry and of Family Medicine & Public Health, University of California, La Jolla, CA, USA;,VA San Diego Center of Excellence for Stress and Mental Health, San Diego, CA, USA
| | - Michael J. Lyons
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
| | - Anthony R. Soltis
- The American Genome Center, Collaborative Health Initiative Research Program, and Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA
| | - Coralie Viollet
- The American Genome Center, Collaborative Health Initiative Research Program, and Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA
| | - Robert J. Ursano
- Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA
| | - Murray B. Stein
- Department of Psychiatry and of Family Medicine & Public Health, University of California, La Jolla, CA, USA
| | - Carol E. Franz
- Department of Psychiatry and of Family Medicine & Public Health, University of California, La Jolla, CA, USA
| | - Yan V. Sun
- Department of Epidemiology, Emory University, Atlanta, GA, USA
| | - Viola Vaccarino
- Department of Epidemiology, Emory University, Atlanta, GA, USA
| | - Nicholas L. Smith
- Seattle Epidemiologic Research and Information Center, Office of Research and Development, U.S. Department of Veteran Affairs, Seattle, WA, USA;,Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Clifton L. Dalgard
- The American Genome Center, Collaborative Health Initiative Research Program, and Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA;,Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA
| | - Matthew D. Wilkerson
- The American Genome Center, Collaborative Health Initiative Research Program, and Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA;,Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA
| | - Harvey B. Pollard
- The American Genome Center, Collaborative Health Initiative Research Program, and Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA;,Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA
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11
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Willoughby EA, McGue M, Iacono WG, Lee JJ. Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring. INTELLIGENCE 2021; 88. [PMID: 34658462 DOI: 10.1016/j.intell.2021.101579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
While adoption studies have provided key insights into the influence of the familial environment on IQ scores of adolescents and children, few have followed adopted offspring long past the time spent living in the family home. To improve confidence about the extent to which shared environment exerts enduring effects on IQ, we estimated genetic and environmental effects on adulthood IQ in a unique sample of 486 biological and adoptive families. These families, tested previously on measures of IQ when offspring averaged age 15, were assessed a second time nearly two decades later ( M offspring age = 32 years). We estimated the proportions of the variance in IQ attributable to environmentally mediated effects of parental IQs, sibling-specific shared environment, and gene-environment covariance to be .01 [95% CI .00, .02], .04 [95% CI .00, .15], and .03 [95% CI .00, .07] respectively; these components jointly accounted for 8 percent of the IQ variance in adulthood. The heritability was estimated to be .42 [95% CI .21, .64]. Together, these findings provide further evidence for the predominance of genetic influences on adult intelligence over any other systematic source of variation.
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Affiliation(s)
- Emily A Willoughby
- University of Minnesota Twin Cities, Department of Psychology 75 E River Rd, Minneapolis, Minnesota 55455
| | - Matt McGue
- University of Minnesota Twin Cities, Department of Psychology 75 E River Rd, Minneapolis, Minnesota 55455
| | - William G Iacono
- University of Minnesota Twin Cities, Department of Psychology 75 E River Rd, Minneapolis, Minnesota 55455
| | - James J Lee
- University of Minnesota Twin Cities, Department of Psychology 75 E River Rd, Minneapolis, Minnesota 55455
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12
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Willoughby EA, McGue M, Iacono WG, Rustichini A, Lee JJ. The role of parental genotype in predicting offspring years of education: evidence for genetic nurture. Mol Psychiatry 2021; 26:3896-3904. [PMID: 31444472 PMCID: PMC7061492 DOI: 10.1038/s41380-019-0494-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 06/10/2019] [Accepted: 06/24/2019] [Indexed: 12/23/2022]
Abstract
Similarities between parent and offspring are widespread in psychology; however, shared genetic variants often confound causal inference for offspring outcomes. A polygenic score (PGS) derived from genome-wide association studies (GWAS) can be used to test for the presence of parental influence that controls for genetic variants shared across generations. We use a PGS for educational attainment (EA3; N ≈ 750 thousand) to predict offspring years of education in a sample of 2517 twins and both parents. We find that within families, the dizygotic twin with the higher PGS is more likely to attain higher education (unstandardized β = 0.32; p < 0.001). Additionally, however, we find an effect of parental genotype on offspring outcome that is independent of the offspring's own genotype; this raises the variance explained in offspring years of education from 9.3 to 11.1% (∆R2 = 0.018, p < 0.001). Controlling for parental IQ or socioeconomic status substantially attenuated or eliminated this effect of parental genotype. These findings suggest a role of environmental factors affected by heritable characteristics of the parents in fostering offspring years of education.
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Affiliation(s)
- Emily A. Willoughby
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - William G. Iacono
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - Aldo Rustichini
- Department of Economics, University of Minnesota Twin Cities, 1925 Fourth Street South, Minneapolis, MN 55455, USA
| | - James J. Lee
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
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13
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A statistical measure for the skewness of X chromosome inactivation for quantitative traits and its application to the MCTFR data. BMC Genom Data 2021; 22:24. [PMID: 34215184 PMCID: PMC8254321 DOI: 10.1186/s12863-021-00978-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 06/17/2021] [Indexed: 11/24/2022] Open
Abstract
Background X chromosome inactivation (XCI) is that one of two chromosomes in mammalian females is silenced during early development of embryos. There has been a statistical measure for the degree of the skewness of XCI for qualitative traits. However, no method is available for such task at quantitative trait loci. Results In this article, we extend the existing statistical measure for the skewness of XCI for qualitative traits, and the likelihood ratio, Fieller’s and delta methods for constructing the corresponding confidence intervals, and make them accommodate quantitative traits. The proposed measure is a ratio of two linear regression coefficients when association exists. Noting that XCI may cause variance heterogeneity of the traits across different genotypes in females, we obtain the point estimate and confidence intervals of the measure by incorporating such information. The hypothesis testing of the proposed methods is also investigated. We conduct extensive simulation studies to assess the performance of the proposed methods. Simulation results demonstrate that the median of the point estimates of the measure is very close to the pre-specified true value. The likelihood ratio and Fieller’s methods control the size well, and have the similar test power and accurate coverage probability, which perform better than the delta method. So far, we are not aware of any association study for the X-chromosomal loci in the Minnesota Center for Twin and Family Research data. So, we apply our proposed methods to these data for their practical use and find that only the rs792959 locus, which is simultaneously associated with the illicit drug composite score and behavioral disinhibition composite score, may undergo XCI skewing. However, this needs to be confirmed by molecular genetics. Conclusions We recommend the Fieller’s method in practical use because it is a non-iterative procedure and has the similar performance to the likelihood ratio method. Supplementary Information The online version contains supplementary material available at 10.1186/s12863-021-00978-z.
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Genome-Wide SNP Analysis for Milk Performance Traits in Indigenous Sheep: A Case Study in the Egyptian Barki Sheep. Animals (Basel) 2021; 11:ani11061671. [PMID: 34205212 PMCID: PMC8228706 DOI: 10.3390/ani11061671] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/28/2021] [Accepted: 05/31/2021] [Indexed: 01/04/2023] Open
Abstract
Simple Summary The Barki sheep is one of the three main breeds in Egypt, which is spread mainly throughout the northwestern coastal zone, which has harsh conditions. Considering the harsh, semi-arid habitat of this breed, milk performance traits such as milk yield and milk composition have a very important role in the feeding of newborn lambs and affect their growth during the early stage of life. In this study, rare milk performance data and genomic information of Barki sheep were used to uncover diversified genomic regions that could explain the variability of milk yield and milk quality traits in the studied population of Barki ewes. Genome-wide analysis identified genomic regions harboring interesting candidate genes such as SLC5A8, NUB1, TBC1D1, KLF3 and ABHD5 for milk yield and PPARA and FBLN1 genes for milk quality traits. The findings offer valuable information for obtaining a better understanding of the genetics of milk performance traits and contribute to the genetic improvement of these traits in Barki sheep. Abstract Sheep milk yield and milk composition traits play an important role in supplying newborn lambs with essential components such as amino acids, energy, vitamins and immune antibodies and are also of interest in terms of the nutritional value of the milk for human consumption. The aim of this study was to identify genomic regions and candidate genes for milk yield and milk composition traits through genome-wide SNP analyses between high and low performing ewes of the Egyptian Barki sheep breed, which is well adapted to the harsh conditions of North-East Africa. Therefore, out of a herd of 111 ewes of the Egyptian Barki sheep breed (IBD = 0.08), ewes representing extremes in milk yield and milk quality traits (n = 25 for each group of animals) were genotyped using the Illumina OvineSNP50 V2 BeadChip. The fixation index (FST) for each SNP was calculated between the diversified groups. FST values were Z-transformed and used to identify putative SNPs for further analysis (Z(FST) > 10). Genome-wide SNP analysis revealed genomic regions covering promising candidate genes related to milk performance traits such as SLC5A8, NUB1, TBC1D1, KLF3 and ABHD5 for milk yield and PPARA and FBLN1 genes for milk quality trait. The results of this study may contribute to the genetic improvement of milk performance traits in Barki sheep breed and to the general understanding of the genetic contribution to variability in milk yield and quality traits.
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Saunders GRB, Liu M, Vrieze S, McGue M, Iacono WG. Mechanisms of parent-child transmission of tobacco and alcohol use with polygenic risk scores: Evidence for a genetic nurture effect. Dev Psychol 2021; 57:796-804. [PMID: 34166022 PMCID: PMC8238311 DOI: 10.1037/dev0001028] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Parent-child similarity is a function of genetic and environmental transmission. In addition, genetic effects not transmitted to offspring may drive parental behavior, thereby affecting the rearing environment of the child. Measuring genetic proclivity directly, through polygenic risk scores (PRSs), provides a way to test for the effect of nontransmitted parental genotype, on offspring outcome, termed a genetic nurture effect-in other words, if and how parental genomes might affect their children through the environment. The current study used polygenic risk scores for smoking initiation, cigarettes per day, and drinks per week to predict substance use in a sample of 3,008 twins, assessed prospectively from age 17-29, and their parents, from the Minnesota Center for Twin and Family Research. Mixed-effects models were used to test for a genetic nurture effect whereby parental PRSs predict offspring tobacco and alcohol use after statistically adjusting for offspring's own PRS. Parental smoking initiation PRS predicted offspring cigarettes per day at age 24 (β = .103, 95% CI [.03, .17]) and alcohol use at age 17 (β = .091, 95% CI [.04, .14]) independent of shared genetics. There was also a suggestive independent association between the parent PRS and offspring smoking at age 17 (β = .096; 95% CI [.02, .17]). Mediation analyses provided some evidence for environmental effects of parental smoking, alcohol use, and family socioeconomic status. These findings, and more broadly the molecular genetic method used, have implications on the identification of environmental effects on developmental outcomes such as substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota
| | - Scott Vrieze
- Department of Psychology, University of Minnesota
| | - Matt McGue
- Department of Psychology, University of Minnesota
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Harper J, Liu M, Malone SM, McGue M, Iacono WG, Vrieze SI. Using multivariate endophenotypes to identify psychophysiological mechanisms associated with polygenic scores for substance use, schizophrenia, and education attainment. Psychol Med 2021; 52:1-11. [PMID: 33731234 PMCID: PMC8448784 DOI: 10.1017/s0033291721000763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND To better characterize brain-based mechanisms of polygenic liability for psychopathology and psychological traits, we extended our previous report (Liu et al. Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci. Psychological Medicine, 2017), focused solely on schizophrenia, to test the association between multivariate psychophysiological candidate endophenotypes (including novel measures of θ/δ oscillatory activity) and a range of polygenic scores (PGSs), namely alcohol/cannabis/nicotine use, an updated schizophrenia PGS (containing 52 more genome-wide significant loci than the PGS used in our previous report) and educational attainment. METHOD A large community-based twin/family sample (N = 4893) was genome-wide genotyped and imputed. PGSs were constructed for alcohol use, regular smoking initiation, lifetime cannabis use, schizophrenia, and educational attainment. Eleven endophenotypes were assessed: visual oddball task event-related electroencephalogram (EEG) measures (target-related parietal P3 amplitude, frontal θ, and parietal δ energy/inter-trial phase clustering), band-limited resting-state EEG power, antisaccade error rate. Principal component analysis exploited covariation among endophenotypes to extract a smaller number of meaningful dimensions/components for statistical analysis. RESULTS Endophenotypes were heritable. PGSs showed expected intercorrelations (e.g. schizophrenia PGS correlated positively with alcohol/nicotine/cannabis PGSs). Schizophrenia PGS was negatively associated with an event-related P3/δ component [β = -0.032, nonparametric bootstrap 95% confidence interval (CI) -0.059 to -0.003]. A prefrontal control component (event-related θ/antisaccade errors) was negatively associated with alcohol (β = -0.034, 95% CI -0.063 to -0.006) and regular smoking PGSs (β = -0.032, 95% CI -0.061 to -0.005) and positively associated with educational attainment PGS (β = 0.031, 95% CI 0.003-0.058). CONCLUSIONS Evidence suggests that multivariate endophenotypes of decision-making (P3/δ) and cognitive/attentional control (θ/antisaccade error) relate to alcohol/nicotine, schizophrenia, and educational attainment PGSs and represent promising targets for future research.
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Affiliation(s)
- Jeremy Harper
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Twin Cities, MN, USA
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota, Twin Cities, MN, USA
| | - Stephen M. Malone
- Department of Psychology, University of Minnesota, Twin Cities, MN, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Twin Cities, MN, USA
| | - William G. Iacono
- Department of Psychology, University of Minnesota, Twin Cities, MN, USA
| | - Scott I. Vrieze
- Department of Psychology, University of Minnesota, Twin Cities, MN, USA
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Arbet J, McGue M, Basu S. A robust and unified framework for estimating heritability in twin studies using generalized estimating equations. Stat Med 2020; 39:3897-3913. [PMID: 32449216 DOI: 10.1002/sim.8564] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 03/13/2020] [Accepted: 04/10/2020] [Indexed: 11/11/2022]
Abstract
The 'heritability' of a phenotype measures the proportion of trait variance due to genetic factors in a population. In the past 50 years, studies with monozygotic and dizygotic twins have estimated heritability for 17,804 traits;1 thus twin studies are popular for estimating heritability. Researchers are often interested in estimating heritability for non-normally distributed outcomes such as binary, counts, skewed or heavy-tailed continuous traits. In these settings, the traditional normal ACE model (NACE) and Falconer's method can produce poor coverage of the true heritability. Therefore, we propose a robust generalized estimating equations (GEE2) framework for estimating the heritability of non-normally distributed outcomes. The traditional NACE and Falconer's method are derived within this unified GEE2 framework, which additionally provides robust standard errors. Although the traditional Falconer's method cannot adjust for covariates, the corresponding 'GEE2-Falconer' can incorporate mean and variance-level covariate effects (e.g. let heritability vary by sex or age). Given a non-normally distributed outcome, the GEE2 models are shown to attain better coverage of the true heritability compared to traditional methods. Finally, a scenario is demonstrated where NACE produces biased estimates of heritability while Falconer remains unbiased. Therefore, we recommend GEE2-Falconer for estimating the heritability of non-normally distributed outcomes in twin studies.
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Affiliation(s)
- Jaron Arbet
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Saonli Basu
- Department of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA
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Seal S, Boatman JA, McGue M, Basu S. Modeling the Dependence Structure in Genome Wide Association Studies of Binary Phenotypes in Family Data. Behav Genet 2020; 50:423-439. [PMID: 32804302 DOI: 10.1007/s10519-020-10010-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Accepted: 07/27/2020] [Indexed: 11/29/2022]
Abstract
Genome-wide association studies (GWASs) are a popular tool for detecting association between genetic variants or single nucleotide polymorphisms (SNPs) and complex traits. Family data introduce complexity due to the non-independence of the family members. Methods for non-independent data are well established, but when the GWAS contains distinct family types, explicit modeling of between-family-type differences in the dependence structure comes at the cost of significantly increased computational burden. The situation is exacerbated with binary traits. In this paper, we perform several simulation studies to compare multiple candidate methods to perform single SNP association analysis with binary traits. We consider generalized estimating equations (GEE), generalized linear mixed models (GLMMs), or generalized least square (GLS) approaches. We study the influence of different working correlation structures for GEE on the GWAS findings and also the performance of different analysis method(s) to conduct a GWAS with binary trait data in families. We discuss the merits of each approach with attention to their applicability in a GWAS. We also compare the performances of the methods on the alcoholism data from the Minnesota Center for Twin and Family Research (MCTFR) study.
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Affiliation(s)
- Souvik Seal
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.
| | - Jeffrey A Boatman
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Saonli Basu
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
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McGue M, Willoughby EA, Rustichini A, Johnson W, Iacono WG, Lee JJ. The Contribution of Cognitive and Noncognitive Skills to Intergenerational Social Mobility. Psychol Sci 2020; 31:835-847. [PMID: 32603210 DOI: 10.1177/0956797620924677] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
We investigated intergenerational educational and occupational mobility in a sample of 2,594 adult offspring and 2,530 of their parents. Participants completed assessments of general cognitive ability and five noncognitive factors related to social achievement; 88% were also genotyped, allowing computation of educational-attainment polygenic scores. Most offspring were socially mobile. Offspring who scored at least 1 standard deviation higher than their parents on both cognitive and noncognitive measures rarely moved down and frequently moved up. Polygenic scores were also associated with social mobility. Inheritance of a favorable subset of parent alleles was associated with moving up, and inheritance of an unfavorable subset was associated with moving down. Parents' education did not moderate the association of offspring's skill with mobility, suggesting that low-skilled offspring from advantaged homes were not protected from downward mobility. These data suggest that cognitive and noncognitive skills as well as genetic factors contribute to the reordering of social standing that takes place across generations.
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Affiliation(s)
- Matt McGue
- Department of Psychology, University of Minnesota
| | | | | | - Wendy Johnson
- Department of Psychology, The University of Edinburgh
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh
| | | | - James J Lee
- Department of Psychology, University of Minnesota
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Evans DM, Moen GH, Hwang LD, Lawlor DA, Warrington NM. Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization. Int J Epidemiol 2020; 48:861-875. [PMID: 30815700 PMCID: PMC6659380 DOI: 10.1093/ije/dyz019] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/08/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother-offspring pairs. METHODS We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown. RESULTS We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes. CONCLUSIONS We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.
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Affiliation(s)
- David M Evans
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia.,Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gunn-Helen Moen
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Liang-Dar Hwang
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
| | - Debbie A Lawlor
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
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Adolescent Externalizing Psychopathology and Its Prospective Relationship to Marijuana Use Development from Age 14 to 30: Replication Across Independent Longitudinal Twin Samples. Behav Genet 2020; 50:139-151. [PMID: 32036544 DOI: 10.1007/s10519-020-09994-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 01/31/2020] [Indexed: 10/25/2022]
Abstract
Externalizing psychopathology in early adolescence is a highly heritable risk factor for drug use, yet how it relates to marijuana use development is not well-characterized. We evaluate this issue in independent twin samples from Colorado (N = 2608) and Minnesota (N = 3630), assessed from adolescence to early adulthood. We used a biometric latent growth model of marijuana use frequency with data from up to five waves of assessment from ages 14 to 30, to examine change in marijuana use and its relationship with a factor model of adolescent externalizing psychopathology. The factor structure of adolescent externalizing psychopathology was similar across samples, as was the association between that common factor and early marijuana use (Minnesota r = 0.67 [0.60, 0.75]; Colorado r = 0.69 [0.59, 0.78]), and increase in use (Minnesota r = 0.18 [0.10, 0.26]; Colorado r = 0.20 [0.07, 0.34]). Early use was moderately heritable in both samples (Minnesota h2 = 0.57 [0.37, 0.79]; Colorado h2 = 0.42 [0.14, 0.73]). Increase in use was highly heritable in Minnesota (h2 = 0.82 [0.72, 0.88]), less so in Colorado (h2 = 0.22 [0.01, 0.66]), and shared environmental effects were larger in Colorado (c2 = 0.55 [0.14, 0.83]) than Minnesota (c2 = 0 [0, 0.06]). We found moderate genetic correlations between externalizing psychopathology and early use in both samples. Finally, additional analyses in the Minnesota sample indicated that marijuana use decreased during the late 20s. This decline is strongly heritable (h2 = 0.73 [0.49, 0.91]) and moderately negatively correlated with adolescent externalizing psychopathology (r = - 0.41 [- 0.54, - 0.28]). Adolescent externalizing psychopathology is genetically correlated with change in late adolescent marijuana use (late teens, early 20s), as well as maintenance of use in early adulthood (late 20 s) even after controlling for the effects of early use.
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Abstract
The Minnesota Center for Twin and Family Research (MCTFR) comprises multiple longitudinal, community-representative investigations of twin and adoptive families that focus on psychological adjustment, personality, cognitive ability and brain function, with a special emphasis on substance use and related psychopathology. The MCTFR includes the Minnesota Twin Registry (MTR), a cohort of twins who have completed assessments in middle and older adulthood; the Minnesota Twin Family Study (MTFS) of twins assessed from childhood and adolescence into middle adulthood; the Enrichment Study (ES) of twins oversampled for high risk for substance-use disorders assessed from childhood into young adulthood; the Adolescent Brain (AdBrain) study, a neuroimaging study of adolescent twins; and the Siblings Interaction and Behavior Study (SIBS), a study of adoptive and nonadoptive families assessed from adolescence into young adulthood. Here we provide a brief overview of key features of these established studies and describe new MCTFR investigations that follow up and expand upon existing studies or recruit and assess new samples, including the MTR Study of Relationships, Personality, and Health (MTR-RPH); the Colorado-Minnesota (COMN) Marijuana Study; the Adolescent Brain Cognitive Development (ABCD) study; the Colorado Online Twins (CoTwins) study and the Children of Twins (CoT) study.
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23
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Lee JJ, McGue M, Iacono WG, Michael AM, Chabris CF. The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling. INTELLIGENCE 2019; 75:48-58. [PMID: 32831433 DOI: 10.1016/j.intell.2019.01.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
There exists a moderate correlation between MRI-measured brain size and the general factor of IQ performance (g), but the question of whether the association reflects a theoretically important causal relationship or spurious confounding remains somewhat open. Previous small studies (n < 100) looking for the persistence of this correlation within families failed to find a tendency for the sibling with the larger brain to obtain a higher test score. We studied the within-family relationship between brain volume and intelligence in the much larger sample provided by the Human Connectome Project (n = 1,022) and found a highly significant correlation (disattenuated ρ = 0.18, p < .001). We replicated this result in the Minnesota Center for Twin and Family Research (n = 2,698), finding a highly significant within-family correlation between head circumference and intelligence (disattenuated ρ = 0.19, p < .001). We also employed novel methods of causal inference relying on summary statistics from genome-wide association studies (GWAS) of head size (n ≈ 10,000) and measures of cognition (257,000 < n < 767,000). Using bivariate LD Score regression, we found a genetic correlation between intracranial volume (ICV) and years of education (EduYears) of 0.41 (p < .001). Using the Latent Causal Variable method, we found a genetic causality proportion of 0.72 (p < .001); thus the genetic correlation arises from an asymmetric pattern, extending to sub-significant loci, of genetic variants associated with ICV also being associated with EduYears but many genetic variants associated with EduYears not being associated with ICV. This is the pattern of genetic results expected from a causal effect of brain size on intelligence. These findings give reason to take up the hypothesis that the dramatic increase in brain volume over the course of human evolution has been the result of natural selection favoring general intelligence.
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Affiliation(s)
- James J Lee
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota Twin Cities, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - Andrew M Michael
- Geisinger Health System, 120 Hamm Drive Suite 2A, Lewisburg, PA 17837, USA.,Duke Institute for Brain Sciences, Duke University, 308 Research Drive, LSRC M051, Durham, NC 27708, USA
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Lee JJ, McGue M, Iacono WG, Chow CC. The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies. Genet Epidemiol 2018; 42:783-795. [PMID: 30251275 DOI: 10.1002/gepi.22161] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 08/03/2018] [Accepted: 08/07/2018] [Indexed: 01/03/2023]
Abstract
To infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with environmental variables that also affect the trait. In this study, we study the properties of linkage disequilibrium (LD) Score regression, a recently developed method for using summary statistics from genome-wide association studies to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are unbiased or conservative under fairly general conditions. This robustness holds in the case of the parent genotype affecting the offspring phenotype through some environmental mechanism, despite the resulting correlation over SNPs between LD Scores and the degree of confounding. Additionally, we demonstrate that LD Score regression can produce reasonably robust estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.
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Affiliation(s)
- James J Lee
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota
| | - Matt McGue
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota
| | - William G Iacono
- Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota
| | - Carson C Chow
- Mathematical Biology Section, Laboratory of Biological Modeling, NIDDK, National Institutes of Health, Bethesda, Maryland
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Carlson J, Locke AE, Flickinger M, Zawistowski M, Levy S, Myers RM, Boehnke M, Kang HM, Scott LJ, Li JZ, Zöllner S. Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans. Nat Commun 2018; 9:3753. [PMID: 30218074 PMCID: PMC6138700 DOI: 10.1038/s41467-018-05936-5] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 07/30/2018] [Indexed: 12/30/2022] Open
Abstract
A detailed understanding of the genome-wide variability of single-nucleotide germline mutation rates is essential to studying human genome evolution. Here, we use ~36 million singleton variants from 3560 whole-genome sequences to infer fine-scale patterns of mutation rate heterogeneity. Mutability is jointly affected by adjacent nucleotide context and diverse genomic features of the surrounding region, including histone modifications, replication timing, and recombination rate, sometimes suggesting specific mutagenic mechanisms. Remarkably, GC content, DNase hypersensitivity, CpG islands, and H3K36 trimethylation are associated with both increased and decreased mutation rates depending on nucleotide context. We validate these estimated effects in an independent dataset of ~46,000 de novo mutations, and confirm our estimates are more accurate than previously published results based on ancestrally older variants without considering genomic features. Our results thus provide the most refined portrait to date of the factors contributing to genome-wide variability of the human germline mutation rate.
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Affiliation(s)
- Jedidiah Carlson
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Adam E Locke
- McDonnell Genome Institute & Department of Medicine, Washington University, St. Louis, MO, 63108, USA
| | - Matthew Flickinger
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Matthew Zawistowski
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hyun Min Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Laura J Scott
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jun Z Li
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, 48109, USA.
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Coombes BJ, Basu S, McGue M. A linear mixed model framework for gene-based gene-environment interaction tests in twin studies. Genet Epidemiol 2018; 42:648-663. [PMID: 30203856 DOI: 10.1002/gepi.22150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 04/25/2018] [Accepted: 04/30/2018] [Indexed: 02/03/2023]
Abstract
Interaction between genes and environments (G×E) can be well investigated in families due to the shared genes and environment among family members. However, the majority of the current tests of G×E interaction between a set of variants and an environment are only suitable for studies with unrelated subjects. In this paper, we extend several G×E interaction tests to a linear mixed model framework to study interaction between a set of correlated environments and a candidate gene in families. The correlated environments can either be modeled separately or jointly in one model. We demonstrate theoretically that the tests developed by modeling correlated environments separately are valid and present a computationally fast alternative to detect G×E interaction in families. For either strategy, we propose treating the genetic main effects as a random effect to reduce the number of main-effect parameters and thus improve the power to detect interactions. Additionally, we propose a generalization of a test of interaction that adaptively sums the interactions using a sequential algorithm. This generalized set of tests, referred to as the sequential algorithm for the sum of powered score (Seq-SPU) family of tests, can be expressed as a weighted version of the SPU. We find that the adaptive version of our test, Seq-aSPU, can outperform aSPU in cases where the interactions effects are in opposite directions. We applied these methods to the Minnesota Center for Twin and Family Research data set and found one significant gene in interaction with four psychosocial environmental factors affecting the alcohol consumption among the twins.
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Affiliation(s)
- Brandon J Coombes
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Saonli Basu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Matt McGue
- Department of Psychology, School of Public Health, University of Minnesota, Minneapolis, Minnesota
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Jiang Y, Chen S, McGuire D, Chen F, Liu M, Iacono WG, Hewitt JK, Hokanson JE, Krauter K, Laakso M, Li KW, Lutz SM, McGue M, Pandit A, Zajac GJM, Boehnke M, Abecasis GR, Vrieze SI, Zhan X, Jiang B, Liu DJ. Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes. PLoS Genet 2018; 14:e1007452. [PMID: 30016313 PMCID: PMC6063450 DOI: 10.1371/journal.pgen.1007452] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Revised: 07/27/2018] [Accepted: 05/25/2018] [Indexed: 11/19/2022] Open
Abstract
Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants. It is of great interest to estimate the joint effects of multiple variants from large scale meta-analyses, in order to fine-map causal variants and understand the genetic architecture for complex traits. The summary association statistics from participating studies in a meta-analysis often contain missing values at some variant sites, as the imputation methods may not work well and the variants with low imputation quality will be filtered out. Missingness is especially likely when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing methods for conditional meta-analysis do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly inflated type-I errors for conditional analysis, which will result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.
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Affiliation(s)
- Yu Jiang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
| | - Sai Chen
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Daniel McGuire
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
| | - Fang Chen
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - William G. Iacono
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - John K. Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - John E. Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Kenneth Krauter
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kevin W. Li
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sharon M. Lutz
- Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Matthew McGue
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Anita Pandit
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Gregory J. M. Zajac
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Michael Boehnke
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Goncalo R. Abecasis
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Scott I. Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Xiaowei Zhan
- Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Bibo Jiang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
- * E-mail: (DJL); (BJ)
| | - Dajiang J. Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
- * E-mail: (DJL); (BJ)
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Conley D, Johnson R, Domingue B, Dawes C, Boardman J, Siegal M. A sibling method for identifying vQTLs. PLoS One 2018; 13:e0194541. [PMID: 29617452 PMCID: PMC5884517 DOI: 10.1371/journal.pone.0194541] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 03/05/2018] [Indexed: 12/11/2022] Open
Abstract
The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Simulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of small degrees of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects.
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Affiliation(s)
- Dalton Conley
- Department of Sociology, Princeton University, Princeton, NJ, United States of America
| | - Rebecca Johnson
- Department of Sociology, Princeton University, Princeton, NJ, United States of America
| | - Ben Domingue
- Graduate School of Education, Stanford University, Stanford, CA, United States of America
| | - Christopher Dawes
- Wilff Family Department of Politics, New York University, New York City, NY, United States of America
| | - Jason Boardman
- Institute for Behavioral Sciences, University of Colorado, Boulder, Boulder, CO, United States of America
| | - Mark Siegal
- Center for Genomics and Systems Biology, New York University, New York University, New York City, NY, United States of America
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Park JY, Wu C, Basu S, McGue M, Pan W. Adaptive SNP-Set Association Testing in Generalized Linear Mixed Models with Application to Family Studies. Behav Genet 2018; 48:55-66. [PMID: 29150721 PMCID: PMC5754233 DOI: 10.1007/s10519-017-9883-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 11/07/2017] [Indexed: 10/18/2022]
Abstract
In genome-wide association studies (GWAS), it has been increasingly recognized that, as a complementary approach to standard single SNP analyses, it may be beneficial to analyze a group of functionally related SNPs together. Among the existent population-based SNP-set association tests, two adaptive tests, the aSPU test and the aSPUpath test, offer a powerful and general approach at the gene- and pathway-levels by data-adaptively combining the results across multiple SNPs (and genes) such that high statistical power can be maintained across a wide range of scenarios. We extend the aSPU and the aSPUpath test to familial data under the framework of the generalized linear mixed models (GLMMs), which can take account of both subject relatedness and possible population structure. As in population-based GWAS, the proposed aSPU and aSPUpath tests require only fitting a single and common GLMM (under the null hypothesis) for all the SNPs, thus are computationally efficient and feasible for large GWAS data. We illustrate our approaches in identifying genes and pathways associated with alcohol dependence in the Minnesota Twin Family Study. The aSPU test detected a gene associated with the trait, in contrast to none by the standard single SNP analysis. Our aSPU test also controlled Type I errors satisfactorily in a small simulation study. We provide R code to conduct the aSPU and aSPUpath tests for familial and other correlated data.
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Affiliation(s)
- Jun Young Park
- Division of Biostatistics, University of Minnesota, A460 Mayo Building, MMC 303, 420 Delaware St. SE, Minneapolis, MN, 55455, USA
| | - Chong Wu
- Division of Biostatistics, University of Minnesota, A460 Mayo Building, MMC 303, 420 Delaware St. SE, Minneapolis, MN, 55455, USA
| | - Saonli Basu
- Division of Biostatistics, University of Minnesota, A460 Mayo Building, MMC 303, 420 Delaware St. SE, Minneapolis, MN, 55455, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, A460 Mayo Building, MMC 303, 420 Delaware St. SE, Minneapolis, MN, 55455, USA.
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Arbet J, McGue M, Chatterjee S, Basu S. Resampling-based tests for Lasso in genome-wide association studies. BMC Genet 2017; 18:70. [PMID: 28738830 PMCID: PMC5525347 DOI: 10.1186/s12863-017-0533-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 06/30/2017] [Indexed: 01/08/2023] Open
Abstract
Background Genome-wide association studies involve detecting association between millions of genetic variants and a trait, which typically use univariate regression to test association between each single variant and the phenotype. Alternatively, Lasso penalized regression allows one to jointly model the relationship between all genetic variants and the phenotype. However, it is unclear how to best conduct inference on the individual Lasso coefficients, especially in high-dimensional settings. Methods We consider six methods for testing the Lasso coefficients: two permutation (Lasso-Ayers, Lasso-PL) and one analytic approach (Lasso-AL) to select the penalty parameter for type-1-error control, residual bootstrap (Lasso-RB), modified residual bootstrap (Lasso-MRB), and a permutation test (Lasso-PT). Methods are compared via simulations and application to the Minnesota Center for Twins and Family Study. Results We show that for finite sample sizes with increasing number of null predictors, Lasso-RB, Lasso-MRB, and Lasso-PT fail to be viable methods of inference. However, Lasso-PL and Lasso-AL remain fast and powerful tools for conducting inference with the Lasso, even in high-dimensions. Conclusion Our results suggest that the proposed permutation selection procedure (Lasso-PL) and the analytic selection method (Lasso-AL) are fast and powerful alternatives to the standard univariate analysis in genome-wide association studies.
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Affiliation(s)
- Jaron Arbet
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, 55455, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, 55455, USA
| | | | - Saonli Basu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, 55455, USA.
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31
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Demkow U, Wolańczyk T. Genetic tests in major psychiatric disorders-integrating molecular medicine with clinical psychiatry-why is it so difficult? Transl Psychiatry 2017; 7:e1151. [PMID: 28608853 PMCID: PMC5537634 DOI: 10.1038/tp.2017.106] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 03/29/2017] [Indexed: 02/06/2023] Open
Abstract
With the advent of post-genomic era, new technologies create extraordinary possibilities for diagnostics and personalized therapy, transforming todays' medicine. Rooted in both medical genetics and clinical psychiatry, the paper is designed as an integrated source of information of the current and potential future application of emerging genomic technologies as diagnostic tools in psychiatry, moving beyond the classical concept of patient approach. Selected approaches are presented, starting from currently used technologies (next-generation sequencing (NGS) and microarrays), followed by newer options (reverse phenotyping). Next, we describe an old concept in a new light (endophenotypes), subsequently coming up with a sophisticated and complex approach (gene networks) ending by a nascent field (computational psychiatry). The challenges and barriers that exist to translate genomic research to real-world patient assessment are further discussed. We emphasize the view that only a paradigm shift can bring a fundamental change in psychiatric practice, allowing to disentangle the intricacies of mental diseases. All the diagnostic methods, as described, are directed at uncovering the integrity of the system including many types of relations within a complex structure. The integrative system approach offers new opportunity to connect genetic background with specific diseases entities, or concurrently, with symptoms regardless of a diagnosis. To advance the field, we propose concerted cross-disciplinary effort to provide a diagnostic platform operating at the general level of genetic pathogenesis of complex-trait psychiatric disorders rather than at the individual level of a specific disease.
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Affiliation(s)
- U Demkow
- Department of Laboratory Diagnostics and Clinical Immunology of Developmental Age, Medical University of Warsaw, Warsaw, Poland,Department of Laboratory Diagnostics and Clinical Immunology of Developmental Age, Medical University of Warsaw, Zwirki i Wigury 63a, Warsaw 02-091, Poland. E-mail:
| | - T Wolańczyk
- Department of Child Psychiatry, Medical University of Warsaw, Warsaw, Poland
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Liu M, Malone SM, Vaidyanathan U, Keller MC, McGue M, Iacono WG, Vrieze SI. Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci. Psychol Med 2017; 47:1116-1125. [PMID: 27995817 PMCID: PMC5352523 DOI: 10.1017/s0033291716003184] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Endophenotypes are laboratory-based measures hypothesized to lie in the causal chain between genes and clinical disorder, and to serve as a more powerful way to identify genes associated with the disorder. One promise of endophenotypes is that they may assist in elucidating the neurobehavioral mechanisms by which an associated genetic polymorphism affects disorder risk in complex traits. We evaluated this promise by testing the extent to which variants discovered to be associated with schizophrenia through large-scale meta-analysis show associations with psychophysiological endophenotypes. METHOD We genome-wide genotyped and imputed 4905 individuals. Of these, 1837 were whole-genome-sequenced at 11× depth. In a community-based sample, we conducted targeted tests of variants within schizophrenia-associated loci, as well as genome-wide polygenic tests of association, with 17 psychophysiological endophenotypes including acoustic startle response and affective startle modulation, antisaccade, multiple frequencies of resting electroencephalogram (EEG), electrodermal activity and P300 event-related potential. RESULTS Using single variant tests and gene-based tests we found suggestive evidence for an association between contactin 4 (CNTN4) and antisaccade and P300. We were unable to find any other variant or gene within the 108 schizophrenia loci significantly associated with any of our 17 endophenotypes. Polygenic risk scores indexing genetic vulnerability to schizophrenia were not related to any of the psychophysiological endophenotypes after correction for multiple testing. CONCLUSIONS The results indicate significant difficulty in using psychophysiological endophenotypes to characterize the genetically influenced neurobehavioral mechanisms by which risk loci identified in genome-wide association studies affect disorder risk.
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Affiliation(s)
- M. Liu
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - S. M. Malone
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | | | - M. C. Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - M. McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - W. G. Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - S. I. Vrieze
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
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33
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A combination test for detection of gene-environment interaction in cohort studies. Genet Epidemiol 2017; 41:396-412. [DOI: 10.1002/gepi.22043] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 02/06/2017] [Accepted: 02/06/2017] [Indexed: 12/24/2022]
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34
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Clark SL, McClay JL, Adkins DE, Kumar G, Aberg KA, Nerella S, Xie L, Collins AL, Crowley JJ, Quackenbush CR, Hilliard CE, Shabalin AA, Vrieze SI, Peterson RE, Copeland WE, Silberg JL, McGue M, Maes H, Iacono WG, Sullivan PF, Costello EJ, van den Oord EJ. Deep Sequencing of 71 Candidate Genes to Characterize Variation Associated with Alcohol Dependence. Alcohol Clin Exp Res 2017; 41:711-718. [PMID: 28196272 DOI: 10.1111/acer.13352] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 02/09/2017] [Indexed: 12/30/2022]
Abstract
BACKGROUND Previous genomewide association studies (GWASs) have identified a number of putative risk loci for alcohol dependence (AD). However, only a few loci have replicated and these replicated variants only explain a small proportion of AD risk. Using an innovative approach, the goal of this study was to generate hypotheses about potentially causal variants for AD that can be explored further through functional studies. METHODS We employed targeted capture of 71 candidate loci and flanking regions followed by next-generation deep sequencing (mean coverage 78X) in 806 European Americans. Regions included in our targeted capture library were genes identified through published GWAS of alcohol, all human alcohol and aldehyde dehydrogenases, reward system genes including dopaminergic and opioid receptors, prioritized candidate genes based on previous associations, and genes involved in the absorption, distribution, metabolism, and excretion of drugs. We performed single-locus tests to determine if any single variant was associated with AD symptom count. Sets of variants that overlapped with biologically meaningful annotations were tested for association in aggregate. RESULTS No single, common variant was significantly associated with AD in our study. We did, however, find evidence for association with several variant sets. Two variant sets were significant at the q-value <0.10 level: a genic enhancer for ADHFE1 (p = 1.47 × 10-5 ; q = 0.019), an alcohol dehydrogenase, and ADORA1 (p = 5.29 × 10-5 ; q = 0.035), an adenosine receptor that belongs to a G-protein-coupled receptor gene family. CONCLUSIONS To our knowledge, this is the first sequencing study of AD to examine variants in entire genes, including flanking and regulatory regions. We found that in addition to protein coding variant sets, regulatory variant sets may play a role in AD. From these findings, we have generated initial functional hypotheses about how these sets may influence AD.
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Affiliation(s)
- Shaunna L Clark
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Joseph L McClay
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Daniel E Adkins
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Gaurav Kumar
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Karolina A Aberg
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Srilaxmi Nerella
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Linying Xie
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Ann L Collins
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - James J Crowley
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Corey R Quackenbush
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Christopher E Hilliard
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Andrey A Shabalin
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
| | - Scott I Vrieze
- Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado.,Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Roseann E Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - William E Copeland
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina
| | - Judy L Silberg
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Hermine Maes
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Elizabeth J Costello
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina
| | - Edwin J van den Oord
- Center for Biomarker Research and Precision Medicine , School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia
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What can time-frequency and phase coherence measures tell us about the genetic basis of P3 amplitude? Int J Psychophysiol 2016; 115:40-56. [PMID: 27871913 DOI: 10.1016/j.ijpsycho.2016.11.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Revised: 10/26/2016] [Accepted: 11/08/2016] [Indexed: 11/21/2022]
Abstract
In a recent comprehensive investigation, we largely failed to identify significant genetic markers associated with P3 amplitude or to corroborate previous associations between P3 and specific single nucleotide polymorphisms (SNPs) or genes. In the present study we extended this line of investigation to examine time-frequency (TF) activity and intertrial phase coherence (ITPC) in the P3 time window, both of which are associated with P3 amplitude. Previous genome-wide research has reported associations between P3-related theta and delta activity and individual genetic variants. A large, population-based sample of 4211 subjects, comprising male and female adolescent twins and their parents, was genotyped for 527,828 single nucleotide polymorphisms (SNPs), from which over six million SNPs were accurately imputed. Heritability estimates were greater for TF energy than ITPC, whether based on biometric models or the combined influence of all measured SNPs (derived from genome-wide complex trait analysis). The magnitude of overlap in the specific SNPs associated with delta energy and ITPC and P3 amplitude was significant. A genome-wide analysis of all SNPs, accompanied by an analysis of approximately 17,600 genes, indicated a region of chromosome 2 around TEKT4 that was significantly associated with theta ITPC. Analysis of candidate SNPs and genes previously reported to be associated with P3 or related phenotypes yielded one association surviving correction for multiple tests: between theta energy and CRHR1. However, we did not obtain significant associations for SNPs implicated in previous genome-wide studies of TF measures. Identifying specific genetic variants associated with P3 amplitude remains a challenge.
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A Test-Replicate Approach to Candidate Gene Research on Addiction and Externalizing Disorders: A Collaboration Across Five Longitudinal Studies. Behav Genet 2016; 46:608-626. [PMID: 27444553 DOI: 10.1007/s10519-016-9800-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 07/06/2016] [Indexed: 10/21/2022]
Abstract
This study presents results from a collaboration across five longitudinal studies seeking to test and replicate models of gene-environment interplay in the development of substance use and externalizing disorders (SUDs, EXT). We describe an overview of our conceptual models, plan for gene-environment interplay analyses, and present main effects results evaluating six candidate genes potentially relevant to SUDs and EXT (MAOA, 5-HTTLPR, COMT, DRD2, DAT1, and DRD4). All samples included rich longitudinal and phenotypic measurements from childhood/adolescence (ages 5-13) through early adulthood (ages 25-33); sample sizes ranged from 3487 in the test sample, to ~600-1000 in the replication samples. Phenotypes included lifetime symptom counts of SUDs (nicotine, alcohol and cannabis), adult antisocial behavior, and an aggregate externalizing disorder composite. Covariates included the first 10 ancestral principal components computed using all autosomal markers in subjects across the data sets, and age at the most recent assessment. Sex, ancestry, and exposure effects were thoroughly evaluated. After correcting for multiple testing, only one significant main effect was found in the test sample, but it was not replicated. Implications for subsequent gene-environment interplay analyses are discussed.
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Mbarek H, Steinberg S, Nyholt D, Gordon S, Miller M, McRae A, Hottenga J, Day F, Willemsen G, de Geus E, Davies G, Martin H, Penninx B, Jansen R, McAloney K, Vink J, Kaprio J, Plomin R, Spector T, Magnusson P, Reversade B, Harris R, Aagaard K, Kristjansson R, Olafsson I, Eyjolfsson G, Sigurdardottir O, Iacono W, Lambalk C, Montgomery G, McGue M, Ong K, Perry J, Martin N, Stefánsson H, Stefánsson K, Boomsma D. Identification of Common Genetic Variants Influencing Spontaneous Dizygotic Twinning and Female Fertility. Am J Hum Genet 2016; 98:898-908. [PMID: 27132594 DOI: 10.1016/j.ajhg.2016.03.008] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 03/14/2016] [Indexed: 02/04/2023] Open
Abstract
Spontaneous dizygotic (DZ) twinning occurs in 1%-4% of women, with familial clustering and unknown physiological pathways and genetic origin. DZ twinning might index increased fertility and has distinct health implications for mother and child. We performed a GWAS in 1,980 mothers of spontaneous DZ twins and 12,953 control subjects. Findings were replicated in a large Icelandic cohort and tested for association across a broad range of fertility traits in women. Two SNPs were identified (rs11031006 near FSHB, p = 1.54 × 10(-9), and rs17293443 in SMAD3, p = 1.57 × 10(-8)) and replicated (p = 3 × 10(-3) and p = 1.44 × 10(-4), respectively). Based on ∼90,000 births in Iceland, the risk of a mother delivering twins increased by 18% for each copy of allele rs11031006-G and 9% for rs17293443-C. A higher polygenic risk score (PRS) for DZ twinning, calculated based on the results of the DZ twinning GWAS, was significantly associated with DZ twinning in Iceland (p = 0.001). A higher PRS was also associated with having children (p = 0.01), greater lifetime parity (p = 0.03), and earlier age at first child (p = 0.02). Allele rs11031006-G was associated with higher serum FSH levels, earlier age at menarche, earlier age at first child, higher lifetime parity, lower PCOS risk, and earlier age at menopause. Conversely, rs17293443-C was associated with later age at last child. We identified robust genetic risk variants for DZ twinning: one near FSHB and a second within SMAD3, the product of which plays an important role in gonadal responsiveness to FSH. These loci contribute to crucial aspects of reproductive capacity and health.
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Schwantes-An TH, Zhang J, Chen LS, Hartz SM, Culverhouse RC, Chen X, Coon H, Frank J, Kamens HM, Konte B, Kovanen L, Latvala A, Legrand LN, Maher BS, Melroy WE, Nelson EC, Reid MW, Robinson JD, Shen PH, Yang BZ, Andrews JA, Aveyard P, Beltcheva O, Brown SA, Cannon DS, Cichon S, Corley RP, Dahmen N, Degenhardt L, Foroud T, Gaebel W, Giegling I, Glatt SJ, Grucza RA, Hardin J, Hartmann AM, Heath AC, Herms S, Hodgkinson CA, Hoffmann P, Hops H, Huizinga D, Ising M, Johnson EO, Johnstone E, Kaneva RP, Kendler KS, Kiefer F, Kranzler HR, Krauter KS, Levran O, Lucae S, Lynskey MT, Maier W, Mann K, Martin NG, Mattheisen M, Montgomery GW, Müller-Myhsok B, Murphy MF, Neale MC, Nikolov MA, Nishita D, Nöthen MM, Nurnberger J, Partonen T, Pergadia ML, Reynolds M, Ridinger M, Rose RJ, Rouvinen-Lagerström N, Scherbaum N, Schmäl C, Soyka M, Stallings MC, Steffens M, Treutlein J, Tsuang M, Wall TL, Wodarz N, Yuferov V, Zill P, Bergen AW, Chen J, Cinciripini PM, Edenberg HJ, Ehringer MA, Ferrell RE, Gelernter J, Goldman D, Hewitt JK, Hopfer CJ, Iacono WG, Kaprio J, Kreek MJ, Kremensky IM, Madden PAF, McGue M, Munafò MR, Philibert RA, Rietschel M, Roy A, Rujescu D, Saarikoski ST, Swan GE, Todorov AA, Vanyukov MM, Weiss RB, Bierut LJ, Saccone NL. Association of the OPRM1 Variant rs1799971 (A118G) with Non-Specific Liability to Substance Dependence in a Collaborative de novo Meta-Analysis of European-Ancestry Cohorts. Behav Genet 2016; 46:151-69. [PMID: 26392368 PMCID: PMC4752855 DOI: 10.1007/s10519-015-9737-3] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Accepted: 08/17/2015] [Indexed: 12/20/2022]
Abstract
The mu1 opioid receptor gene, OPRM1, has long been a high-priority candidate for human genetic studies of addiction. Because of its potential functional significance, the non-synonymous variant rs1799971 (A118G, Asn40Asp) in OPRM1 has been extensively studied, yet its role in addiction has remained unclear, with conflicting association findings. To resolve the question of what effect, if any, rs1799971 has on substance dependence risk, we conducted collaborative meta-analyses of 25 datasets with over 28,000 European-ancestry subjects. We investigated non-specific risk for "general" substance dependence, comparing cases dependent on any substance to controls who were non-dependent on all assessed substances. We also examined five specific substance dependence diagnoses: DSM-IV alcohol, opioid, cannabis, and cocaine dependence, and nicotine dependence defined by the proxy of heavy/light smoking (cigarettes-per-day >20 vs. ≤ 10). The G allele showed a modest protective effect on general substance dependence (OR = 0.90, 95% C.I. [0.83-0.97], p value = 0.0095, N = 16,908). We observed similar effects for each individual substance, although these were not statistically significant, likely because of reduced sample sizes. We conclude that rs1799971 contributes to mechanisms of addiction liability that are shared across different addictive substances. This project highlights the benefits of examining addictive behaviors collectively and the power of collaborative data sharing and meta-analyses.
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Affiliation(s)
- Tae-Hwi Schwantes-An
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
- Genometrics Section, Computational and Statistical Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, US National Institutes of Health (NIH), Baltimore, MD, 21224, USA
| | - Juan Zhang
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
- Key Laboratory of Brain Function and Disease, School of Life Sciences, Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Li-Shiun Chen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Sarah M Hartz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Robert C Culverhouse
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Xiangning Chen
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Hilary Coon
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, 84108, USA
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, 68159, Mannheim, Germany
| | - Helen M Kamens
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, 80309, USA
- Department of Integrative Physiology, University of Colorado, Boulder, CO, 80309, USA
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Bettina Konte
- Department of Psychiatry, Universitätsklinikum Halle (Saale), 06112, Halle (Saale), Germany
| | - Leena Kovanen
- Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Antti Latvala
- Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
| | - Lisa N Legrand
- Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Brion S Maher
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Whitney E Melroy
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, 80309, USA
- Department of Integrative Physiology, University of Colorado, Boulder, CO, 80309, USA
| | - Elliot C Nelson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Mark W Reid
- Oregon Research Institute, Eugene, OR, 97403, USA
| | - Jason D Robinson
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Pei-Hong Shen
- Section of Human Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, 20892, USA
| | - Bao-Zhu Yang
- Department of Psychiatry, Yale University, New Haven, CT, 06516, USA
| | | | - Paul Aveyard
- Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, United Kingdom
| | - Olga Beltcheva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, 1431, Sofia, Bulgaria
| | - Sandra A Brown
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Dale S Cannon
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, 84108, USA
| | - Sven Cichon
- Department. of Genomics, Life and Brain Center, Institute of Human Genetics, University of Bonn, Bonn, 53127, Germany
- Division of Medical Genetics, Department of Biomedicine, University Hospital Basel, University of Basel, Basel, 4003, Switzerland
| | - Robin P Corley
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, 80309, USA
| | - Norbert Dahmen
- Ökumenisches Hainich-Klinikum, Mühlhausen/Thüringen, Germany
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre, University of New South Wales, Randwick, NSW, 2031, Australia
- School of Population and Global Health, University of Melbourne, Melbourne, 3010, Australia
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | - Ina Giegling
- Department of Psychiatry, Universitätsklinikum Halle (Saale), 06112, Halle (Saale), Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Richard A Grucza
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jill Hardin
- Center for Health Sciences, Biosciences Division, SRI International, Menlo Park, CA, 94025, USA
| | - Annette M Hartmann
- Department of Psychiatry, Universitätsklinikum Halle (Saale), 06112, Halle (Saale), Germany
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Stefan Herms
- Department. of Genomics, Life and Brain Center, Institute of Human Genetics, University of Bonn, Bonn, 53127, Germany
- Division of Medical Genetics, Department of Biomedicine, University Hospital Basel, University of Basel, Basel, 4003, Switzerland
| | - Colin A Hodgkinson
- Section of Human Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, 20892, USA
| | - Per Hoffmann
- Department. of Genomics, Life and Brain Center, Institute of Human Genetics, University of Bonn, Bonn, 53127, Germany
- Division of Medical Genetics, Department of Biomedicine, University Hospital Basel, University of Basel, Basel, 4003, Switzerland
| | - Hyman Hops
- Oregon Research Institute, Eugene, OR, 97403, USA
| | - David Huizinga
- Institute of Behavioral Science, University of Colorado, Boulder, CO, 80309, USA
| | - Marcus Ising
- Max-Planck-Institute of Psychiatry, 80804, Munich, Germany
| | - Eric O Johnson
- Behavioral Health Research Division, Research Triangle Institute International, Durham, NC, 27709, USA
| | - Elaine Johnstone
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Radka P Kaneva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, 1431, Sofia, Bulgaria
| | - Kenneth S Kendler
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Falk Kiefer
- Department of Addictive Behavior and Addiction Medicine, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, 68159, Mannheim, Germany
| | - Henry R Kranzler
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ken S Krauter
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, 80309, USA
- Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, CO, 80309, USA
| | - Orna Levran
- Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York, 10065, USA
| | - Susanne Lucae
- Max-Planck-Institute of Psychiatry, 80804, Munich, Germany
| | - Michael T Lynskey
- Addictions Department, Institute of Psychiatry, King's College London, London, SE5 8BB, UK
| | | | - Karl Mann
- Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, 68159, Mannheim, Germany
| | - Nicholas G Martin
- Department of Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, QLD, 4029, Australia
| | - Manuel Mattheisen
- Department. of Genomics, Life and Brain Center, Institute of Human Genetics, University of Bonn, Bonn, 53127, Germany
- Harvard School of Public Health, Boston, MA, 02115, USA
- Aarhus University, Aarhus, 8000, Denmark
| | - Grant W Montgomery
- Department of Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, QLD, 4029, Australia
| | | | - Michael F Murphy
- Childhood Cancer Research Group, University of Oxford, Oxford, OX3 7LG, UK
| | - Michael C Neale
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Momchil A Nikolov
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, 1431, Sofia, Bulgaria
| | - Denise Nishita
- Center for Health Sciences, Biosciences Division, SRI International, Menlo Park, CA, 94025, USA
| | - Markus M Nöthen
- Department. of Genomics, Life and Brain Center, Institute of Human Genetics, University of Bonn, Bonn, 53127, Germany
| | - John Nurnberger
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Timo Partonen
- Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Michele L Pergadia
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Maureen Reynolds
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Monika Ridinger
- Department of Psychiatry, University Medical Center Regensburg, University of Regensburg, 8548, Regensburg, Germany
- Psychiatric Hospital, Konigsfelden, Windisch, Switzerland
| | - Richard J Rose
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Noora Rouvinen-Lagerström
- Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Norbert Scherbaum
- Addiction Research Group at the Department of Psychiatry and Psychotherapy, LVR Hospital Essen, University of Duisburg-Essen, 45147, Essen, Germany
| | - Christine Schmäl
- Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, 68159, Mannheim, Germany
| | - Michael Soyka
- Department of Psychiatry, University of Munich, 3860, Munich, Germany
- Private Hospital Meiringen, Meiringen, Switzerland
| | - Michael C Stallings
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, 80309, USA
- Department of Psychology & Neuroscience, University of Colorado, Boulder, CO, 80309, USA
| | - Michael Steffens
- Research Department, Federal Institute for Drugs and Medical Devices (BfArM), Kurt-Georg-Kiesinger-Allee 3, 53175, Bonn, Germany
| | - Jens Treutlein
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, 68159, Mannheim, Germany
| | - Ming Tsuang
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Tamara L Wall
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Norbert Wodarz
- Department of Psychiatry, University Medical Center Regensburg, University of Regensburg, 8548, Regensburg, Germany
| | - Vadim Yuferov
- Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York, 10065, USA
| | | | - Andrew W Bergen
- Center for Health Sciences, Biosciences Division, SRI International, Menlo Park, CA, 94025, USA
| | - Jingchun Chen
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Paul M Cinciripini
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Marissa A Ehringer
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, 80309, USA
- Department of Integrative Physiology, University of Colorado, Boulder, CO, 80309, USA
| | - Robert E Ferrell
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University, New Haven, CT, 06516, USA
- Department of Genetics, Yale University, New Haven, CT, 06516, USA
- Department of Neurobiology, Yale University, New Haven, CT, 06516, USA
| | - David Goldman
- Section of Human Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, 20892, USA
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, 80309, USA
- Department of Psychology & Neuroscience, University of Colorado, Boulder, CO, 80309, USA
| | - Christian J Hopfer
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Jaakko Kaprio
- Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, 00271, Finland
- Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
- Institute for Molecular Medicine FIMM, University of Helsinki, 00014, Helsinki, Finland
| | - Mary Jeanne Kreek
- Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York, 10065, USA
| | - Ivo M Kremensky
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, 1431, Sofia, Bulgaria
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Marcus R Munafò
- MRC Integrative Epidemiology Unit, UK Centre for Tobacco and Alcohol Studies, and School of Experimental Psychology, University of Bristol, Bristol, BS8 1TU, UK
| | | | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, 68159, Mannheim, Germany
| | - Alec Roy
- Psychiatry Service, Department of Veteran Affairs, New Jersey VA Health Care System, East Orange, NJ, 07018, USA
| | - Dan Rujescu
- Department of Psychiatry, Universitätsklinikum Halle (Saale), 06112, Halle (Saale), Germany
| | - Sirkku T Saarikoski
- Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Gary E Swan
- Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Alexandre A Todorov
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Michael M Vanyukov
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Robert B Weiss
- Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT, 84112, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Nancy L Saccone
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA.
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Malone SM, Vaidyanathan U, Basu S, Miller MB, McGue M, Iacono WG. Heritability and molecular-genetic basis of the P3 event-related brain potential: a genome-wide association study. Psychophysiology 2015; 51:1246-58. [PMID: 25387705 DOI: 10.1111/psyp.12345] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
P3 amplitude is a candidate endophenotype for disinhibitory psychopathology, psychosis, and other disorders. The present study is a comprehensive analysis of the behavioral- and molecular-genetic basis of P3 amplitude and a P3 genetic factor score in a large community sample (N = 4,211) of adolescent twins and their parents, genotyped for 527,829 single nucleotide polymorphisms (SNPs). Biometric models indicated that as much as 65% of the variance in each measure was due to additive genes. All SNPs in aggregate accounted for approximately 40% to 50% of the heritable variance. However, analyses of individual SNPs did not yield any significant associations. Analyses of individual genes did not confirm previous associations between P3 amplitude and candidate genes but did yield a novel association with myelin expression factor 2 (MYEF2). Main effects of individual variants may be too small to be detected by GWAS without larger samples.
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Affiliation(s)
- Stephen M Malone
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
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Malone SM, Burwell SJ, Vaidyanathan U, Miller MB, McGue M, Iacono WG. Heritability and molecular-genetic basis of resting EEG activity: a genome-wide association study. Psychophysiology 2015; 51:1225-45. [PMID: 25387704 DOI: 10.1111/psyp.12344] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Several EEG parameters are potential endophenotypes for different psychiatric disorders. The present study consists of a comprehensive behavioral- and molecular-genetic analysis of such parameters in a large community sample (N = 4,026) of adolescent twins and their parents, genotyped for 527,829 single nucleotide polymorphisms (SNPs). Biometric heritability estimates ranged from .49 to .85, with a median of .78. The additive effect of all SNPs (SNP heritability) varied across electrodes. Although individual SNPs were not significantly associated with EEG parameters, several genes were associated with delta power. We also obtained an association between the GABRA2 gene and beta power (p < .014), consistent with findings reported by others, although this did not survive Bonferroni correction. If EEG parameters conform to a largely polygenic model of inheritance, larger sample sizes will be required to detect individual variants reliably.
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Affiliation(s)
- Stephen M Malone
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
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Vaidyanathan U, Isen JD, Malone SM, Miller MB, McGue M, Iacono WG. Heritability and molecular genetic basis of electrodermal activity: a genome-wide association study. Psychophysiology 2015; 51:1259-71. [PMID: 25387706 DOI: 10.1111/psyp.12346] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The molecular genetic basis of electrodermal activity (EDA) was analyzed using 527,829 single nucleotide polymorphisms (SNPs) in a large population-representative sample of twins and parents (N = 4,424) in relation to various EDA indices. Biometric analyses suggested that approximately 50% or more of variance in all EDA indices was heritable. The combined effect of all SNPs together accounted for a significant amount of variance in each index, affirming their polygenic basis and heritability. However, none of the SNPs were genome-wide significant for any EDA index. Previously reported SNP associations with disorders such as substance dependence or schizophrenia, which have been linked to EDA abnormalities, were not significant; nor were associations between EDA and genes in specific neurotransmitter systems. These results suggest that EDA is influenced by multiple genes rather than by polymorphisms with large effects.
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Affiliation(s)
- Uma Vaidyanathan
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
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Premorbid risk factors for major depressive disorder: are they associated with early onset and recurrent course? Dev Psychopathol 2015; 26:1477-93. [PMID: 25422974 DOI: 10.1017/s0954579414001151] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Premorbid risk for major depressive disorder (MDD) and predictors of an earlier onset and recurrent course were examined in two studies in a large, community-based sample of parents and offspring, prospectively assessed from late childhood into adulthood. In Study 1 (N = 2,764 offspring and their parents), parental psychiatric status, offspring personality at age 11, and age 11 offspring internalizing and externalizing symptoms predicted the subsequent development of MDD, as did poor quality parent-child relationships, poor academic functioning, early pubertal development, and childhood maltreatment by age 11. Parental MDD and adult antisocial behavior, offspring negative emotionality and disconstraint, externalizing symptoms, and childhood maltreatment predicted an earlier onset of MDD, after accounting for course; lower positive emotionality, trait anxiety, and childhood maltreatment predicted recurrent MDD, after accounting for age of onset. In Study 2 (N = 7,146), we examined molecular genetic risk for MDD by extending recent reports of associations with glutamatergic system genes. We failed to confirm associations with MDD using either individual single nucleotide polymorphism based tests or gene-based analyses. Overall, results speak to the pervasiveness of risk for MDD, as well as specific risk for early onset MDD; risk for recurrent MDD appears to be largely a function of its often earlier onset.
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Derringer J, Corley RP, Haberstick BC, Young SE, Demmitt BA, Howrigan DP, Kirkpatrick RM, Iacono WG, McGue M, Keller MC, Brown S, Tapert S, Hopfer CJ, Stallings MC, Crowley TJ, Rhee SH, Krauter K, Hewitt JK, McQueen MB. Genome-Wide Association Study of Behavioral Disinhibition in a Selected Adolescent Sample. Behav Genet 2015; 45:375-81. [PMID: 25637581 PMCID: PMC4459903 DOI: 10.1007/s10519-015-9705-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Accepted: 01/07/2015] [Indexed: 10/24/2022]
Abstract
Behavioral disinhibition (BD) is a quantitative measure designed to capture the heritable variation encompassing risky and impulsive behaviors. As a result, BD represents an ideal target for discovering genetic loci that predispose individuals to a wide range of antisocial behaviors and substance misuse that together represent a large cost to society as a whole. Published genome-wide association studies (GWAS) have examined specific phenotypes that fall under the umbrella of BD (e.g. alcohol dependence, conduct disorder); however no GWAS has specifically examined the overall BD construct. We conducted a GWAS of BD using a sample of 1,901 adolescents over-selected for characteristics that define high BD, such as substance and antisocial behavior problems, finding no individual locus that surpassed genome-wide significance. Although no single SNP was significantly associated with BD, restricted maximum likelihood analysis estimated that 49.3 % of the variance in BD within the Caucasian sub-sample was accounted for by the genotyped SNPs (p = 0.06). Gene-based tests identified seven genes associated with BD (p ≤ 2.0 × 10(-6)). Although the current study was unable to identify specific SNPs or pathways with replicable effects on BD, the substantial sample variance that could be explained by all genotyped SNPs suggests that larger studies could successfully identify common variants associated with BD.
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Affiliation(s)
- Jaime Derringer
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, 61820, USA,
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Wang B, Gao W, Yu C, Cao W, Lv J, Wang S, Pang Z, Cong L, Wang H, Wu X, Li L. Determination of Zygosity in Adult Chinese Twins Using the 450K Methylation Array versus Questionnaire Data. PLoS One 2015; 10:e0123992. [PMID: 25927701 PMCID: PMC4415785 DOI: 10.1371/journal.pone.0123992] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Accepted: 02/25/2015] [Indexed: 11/19/2022] Open
Abstract
Previous studies have shown that both single nucleotide polymorphisms (SNPs) and questionnaires-based method can be used for twin zygosity determination, but few validation studies have been conducted using Chinese populations. In the current study, we recruited 192 same sex Chinese adult twin pairs to evaluate the validity of using genetic markers-based method and questionnaire-based method in zygosity determination. We considered the relatedness analysis based on more than 0.6 million SNPs genotyping as the golden standards for zygosity determination. After quality control, qualified twins were left for relatedness analysis based on identical by descent calculation. Then those same sex twin pairs were included in the zygosity questionnaire validation analysis. Logistic regression model was applied to assess the discriminant ability of age, sex and the three questions in zygosity determination. Leave one out cross-validation was used as a measurement of internal validation. The results of zygosity determination based on 65 SNPs in 450k methylation array were all consistent with genotyping. Age, gender, questions of appearance confused by strangers and previously perceived zygosity consisted of the most predictable model with a consistency rate of 0.8698, cross validation predictive error of 0.1347. For twin studies with genotyping and\or 450k methylation array, there would be no need to conduct other zygosity testing for the sake of costs consideration.
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Affiliation(s)
- Biqi Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Shengfeng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zengchang Pang
- Qingdao Center for Diseases Control and Prevention, Qingdao, 266033, China
| | - Liming Cong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, 610041, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- * E-mail:
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Vaidyanathan U, Malone SM, Donnelly JM, Hammer MA, Miller MB, McGue M, Iacono WG. Heritability and molecular genetic basis of antisaccade eye tracking error rate: a genome-wide association study. Psychophysiology 2014; 51:1272-84. [PMID: 25387707 PMCID: PMC4238043 DOI: 10.1111/psyp.12347] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antisaccade deficits reflect abnormalities in executive function linked to various disorders including schizophrenia, externalizing psychopathology, and neurological conditions. We examined the genetic bases of antisaccade error in a sample of community-based twins and parents (N = 4,469). Biometric models showed that about half of the variance in the antisaccade response was due to genetic factors and half due to nonshared environmental factors. Molecular genetic analyses supported these results, showing that the heritability accounted for by common molecular genetic variants approximated biometric estimates. Genome-wide analyses revealed several SNPs as well as two genes-B3GNT7 and NCL-on Chromosome 2 associated with antisaccade error. SNPs and genes hypothesized to be associated with antisaccade error based on prior work, although generating some suggestive findings for MIR137, GRM8, and CACNG2, could not be confirmed.
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Affiliation(s)
- Uma Vaidyanathan
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
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Iacono WG, Malone SM, Vaidyanathan U, Vrieze SI. Genome-wide scans of genetic variants for psychophysiological endophenotypes: a methodological overview. Psychophysiology 2014; 51:1207-24. [PMID: 25387703 PMCID: PMC4231489 DOI: 10.1111/psyp.12343] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
This article provides an introductory overview of the investigative strategy employed to evaluate the genetic basis of 17 endophenotypes examined as part of a 20-year data collection effort from the Minnesota Center for Twin and Family Research. Included are characterization of the study samples, descriptive statistics for key properties of the psychophysiological measures, and rationale behind the steps taken in the molecular genetic study design. The statistical approach included (a) biometric analysis of twin and family data, (b) heritability analysis using 527,829 single nucleotide polymorphisms (SNPs), (c) genome-wide association analysis of these SNPs and 17,601 autosomal genes, (d) follow-up analyses of candidate SNPs and genes hypothesized to have an association with each endophenotype, (e) rare variant analysis of nonsynonymous SNPs in the exome, and (f) whole genome sequencing association analysis using 27 million genetic variants. These methods were used in the accompanying empirical articles comprising this special issue, Genome-Wide Scans of Genetic Variants for Psychophysiological Endophenotypes.
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Affiliation(s)
- William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
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Vaidyanathan U, Malone SM, Miller MB, McGue M, Iacono WG. Heritability and molecular genetic basis of acoustic startle eye blink and affectively modulated startle response: a genome-wide association study. Psychophysiology 2014; 51:1285-99. [PMID: 25387708 PMCID: PMC4231542 DOI: 10.1111/psyp.12348] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Acoustic startle responses have been studied extensively in relation to individual differences and psychopathology. We examined three indices of the blink response in a picture-viewing paradigm-overall startle magnitude across all picture types, and aversive and pleasant modulation scores-in 3,323 twins and parents. Biometric models and molecular genetic analyses showed that half the variance in overall startle was due to additive genetic effects. No single nucleotide polymorphism was genome-wide significant, but GRIK3 produced a significant effect when examined as part of a candidate gene set. In contrast, emotion modulation scores showed little evidence of heritability in either biometric or molecular genetic analyses. However, in a genome-wide scan, PARP14 produced a significant effect for aversive modulation. We conclude that, although overall startle retains potential as an endophenotype, emotion-modulated startle does not.
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Affiliation(s)
- Uma Vaidyanathan
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
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Vrieze SI, Malone SM, Pankratz N, Vaidyanathan U, Miller MB, Kang HM, McGue M, Abecasis G, Iacono WG. Genetic associations of nonsynonymous exonic variants with psychophysiological endophenotypes. Psychophysiology 2014; 51:1300-8. [PMID: 25387709 PMCID: PMC4231532 DOI: 10.1111/psyp.12349] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
We mapped ∼85,000 rare nonsynonymous exonic single nucleotide polymorphisms (SNPs) to 17 psychophysiological endophenotypes in 4,905 individuals, including antisaccade eye movements, resting EEG, P300 amplitude, electrodermal activity, affect-modulated startle eye blink. Nonsynonymous SNPs are predicted to directly change or disrupt proteins encoded by genes and are expected to have significant biological consequences. Most such variants are rare, and new technologies can efficiently assay them on a large scale. We assayed 247,870 mostly rare SNPs on an Illumina exome array. Approximately 85,000 of the SNPs were polymorphic, rare (MAF < .05), and nonsynonymous. Single variant association tests identified a SNP in the PARD3 gene associated with theta resting EEG power. The sequence kernel association test, a gene-based test, identified a gene PNPLA7 associated with pleasant difference startle, the difference in startle magnitude between pleasant and neutral images. No other single nonsynonymous variant, or gene-based group of variants, was strongly associated with any endophenotype.
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Affiliation(s)
- Scott I. Vrieze
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Stephen M. Malone
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Uma Vaidyanathan
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michael B. Miller
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Hyun Min Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Gonçalo Abecasis
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - William G. Iacono
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
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Vrieze SI, Malone SM, Vaidyanathan U, Kwong A, Kang HM, Zhan X, Flickinger M, Irons D, Jun G, Locke AE, Pistis G, Porcu E, Levy S, Myers RM, Oetting W, McGue M, Abecasis G, Iacono WG. In search of rare variants: preliminary results from whole genome sequencing of 1,325 individuals with psychophysiological endophenotypes. Psychophysiology 2014; 51:1309-20. [PMID: 25387710 PMCID: PMC4231480 DOI: 10.1111/psyp.12350] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Whole genome sequencing was completed on 1,325 individuals from 602 families, identifying 27 million autosomal variants. Genetic association tests were conducted for those individuals who had been assessed for one or more of 17 endophenotypes (N range = 802-1,185). No significant associations were found. These 27 million variants were then imputed into the full sample of individuals with psychophysiological data (N range = 3,088-4,469) and again tested for associations with the 17 endophenotypes. No association was significant. Using a gene-based variable threshold burden test of nonsynonymous variants, we obtained five significant associations. These findings are preliminary and call for additional analysis of this rich sample. We argue that larger samples, alternative study designs, and additional bioinformatics approaches will be necessary to discover associations between these endophenotypes and genomic variation.
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Affiliation(s)
- Scott I Vrieze
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
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Results of a "GWAS plus:" general cognitive ability is substantially heritable and massively polygenic. PLoS One 2014; 9:e112390. [PMID: 25383866 PMCID: PMC4226546 DOI: 10.1371/journal.pone.0112390] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Accepted: 05/04/2014] [Indexed: 11/24/2022] Open
Abstract
We carried out a genome-wide association study (GWAS) for general cognitive ability (GCA) plus three other analyses of GWAS data that aggregate the effects of multiple single-nucleotide polymorphisms (SNPs) in various ways. Our multigenerational sample comprised 7,100 Caucasian participants, drawn from two longitudinal family studies, who had been assessed with an age-appropriate IQ test and had provided DNA samples passing quality screens. We conducted the GWAS across ∼2.5 million SNPs (both typed and imputed), using a generalized least-squares method appropriate for the different family structures present in our sample, and subsequently conducted gene-based association tests. We also conducted polygenic prediction analyses under five-fold cross-validation, using two different schemes of weighting SNPs. Using parametric bootstrapping, we assessed the performance of this prediction procedure under the null. Finally, we estimated the proportion of variance attributable to all genotyped SNPs as random effects with software GCTA. The study is limited chiefly by its power to detect realistic single-SNP or single-gene effects, none of which reached genome-wide significance, though some genomic inflation was evident from the GWAS. Unit SNP weights performed about as well as least-squares regression weights under cross-validation, but the performance of both increased as more SNPs were included in calculating the polygenic score. Estimates from GCTA were 35% of phenotypic variance at the recommended biological-relatedness ceiling. Taken together, our results concur with other recent studies: they support a substantial heritability of GCA, arising from a very large number of causal SNPs, each of very small effect. We place our study in the context of the literature–both contemporary and historical–and provide accessible explication of our statistical methods.
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