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Introduction to the Special Issue on Statistical Genetic Methods for Human Complex Traits

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Notes

  1. The workshop was first held in Leuven in 1987 (David Evans, Sarah Medland, Elizabeth Prom-Wormley).

References

  • Balbona JV, Kim Y, Keller MC (2021) Estimation of parental effects using polygenic scores. Behav Genet. https://doi.org/10.1007/s10519-020-10032-w

  • Bates TC, Maher BS, Medland SE, McAloney K, Wright MJ, Hansell NK, Kendler KS, Martin NG, Gillespie NA (2018) The nature of nurture: using a virtual-parent design to test parenting effects on children’s educational attainment in genotyped families. Twin Res Hum Genet 21(2):73–83

    Article  Google Scholar 

  • Boomsma DI, van Beijsterveldt T, Odintsova VV, Neale MC, Dolan CV (2020) Genetically informed regression analysis: application to aggression prediction by inattention and hyperactivity in children and adults. Behav Genet. https://doi.org/10.1007/s10519-020-10025-9

  • Brumpton B, Sanderson E, Heilbron K, Hartwig FP, Harrison S, Vie GA, Cho Y, Howe LD, Hughes A, Boomsma DI, Havdahl A, Hopper J, Neale M, Nivard MG, Pedersen NL, Reynolds CA, Tucker-Drob EM, Grotzinger A, Howe L, Morris T, Li S, Within-family Consortium; 23andMe Research Team, Auton A, Windmeijer F, Chen WM, Bjorngaard JH, Hveem K, Willer C, Evans DM, Kaprio J, Davey Smith G, Asvold BO, Hemani G, Davies NM (2020) Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 11(1):3519

    Article  Google Scholar 

  • Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, ReproGen C, Psychiatric Genomics C, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control C, Duncan L, Perry JR, Patterson N, Robinson EB, Daly MJ, Price AL, Neale BM (2015a) An atlas of genetic correlations across human diseases and traits. Nat Genet 47(11):1236–1241

    Article  Google Scholar 

  • Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics C, Patterson N, Daly MJ, Price AL, Neale BM (2015b) LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47(3):291–295

    Article  Google Scholar 

  • Chen J, Bacelis J, Sole-Navais P, Srivastava A, Juodakis J, Rouse A, Hallman M, Teramo K, Melbye M, Feenstra B, Freathy RM, Smith GD, Lawlor DA, Murray JC, Williams SM, Jacobsson B, Muglia LJ, Zhang G (2020) Dissecting maternal and fetal genetic effects underlying the associations between maternal phenotypes, birth outcomes, and adult phenotypes: a mendelian-randomization and haplotype-based genetic score analysis in 10,734 mother-infant pairs. PLoS Med 17(8):e1003305

    Article  Google Scholar 

  • de Vries LP, van Beijsterveldt T, Maes H, Colodro-Conde L, Bartels M (2021) Genetic influences on the covariance and genetic correlations in a bivariate twin model: an application to well-being. Behav Genet. https://doi.org/10.1007/s10519-021-10046-y

  • Dolan CV, Huijskens RCA, Minica CC, Neale MC, Boomsma DI (2021) Incorporating polygenic risk scores in the ACE twin model to estimate A–C covariance. Behav Genet. https://doi.org/10.1007/s10519-020-10035-7

  • Eaves LJ, Pourcain BS, Smith GD, York TP, Evans DM (2014) Resolving the effects of maternal and offspring genotype on dyadic outcomes in genome wide complex trait analysis (“M-GCTA”). Behav Genet 44(5):445–455

    Article  Google Scholar 

  • Eilertsen EM, Jami ES, McAdams TA, Hannigan LJ, Havdahl AS, Magnus P, Evans DM, Ystrom E (2021) Direct and indirect effects of maternal, paternal, and offspring genotypes: Trio-GCTA. Behav Genet. https://doi.org/10.1007/s10519-020-10036-6

  • Evans DM (2020) The Boulder workshop question box. Behav Genet. https://doi.org/10.1007/s10519-020-10022-y

  • Evans DM, Moen GH, Hwang LD, Lawlor DA, Warrington NM (2019) Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization. Int J Epidemiol 48(3):861–875

    Article  Google Scholar 

  • Grotzinger AD, Rhemtulla M, de Vlaming R, Ritchie SJ, Mallard TT, Hill WD, Ip HF, Marioni RE, McIntosh AM, Deary IJ, Koellinger PD, Harden KP, Nivard MG, Tucker-Drob EM (2019) Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav 3(5):513–525

    Article  Google Scholar 

  • Hunter MD (2021) Multilevel modeling in classical twin and modern molecular behavior genetics. Behav Genet. https://doi.org/10.1007/s10519-021-10045-z

  • Hwang LD, Tubbs JD, Luong J, Lundberg M, Moen GH, Wang G, Warrington NM, Sham PC, Cuellar-Partida G, Evans DM (2020) Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs. PLoS Genet 16(10):e1009154

    Article  Google Scholar 

  • Hwang LD, Mitchell BL, Medland SE, Martin NG, Neale MC, Evans DM (2021) The augmented classical twin design: incorporating genome-wide identity by descent sharing into twin studies in order to model violations of the equal environments assumption. Behav Genet. https://doi.org/10.1007/s10519-021-10044-0

  • Joreskog KG, Sorbom D (1981) LISREL analysis of linear structural relationships by maximum likelihood and least squares methods. National Educational Resources, Chicago

    Google Scholar 

  • Kim Y, Balbona JV, Keller MC (2020) Bias and precision of parameter estimates from models using polygenic scores to estimate environmental and genetic parental influences. Behav Genet. https://doi.org/10.1007/s10519-020-10033-9

  • Kirkpatrick RM, Pritikin JN, Hunter MD, Neale MC (2021) Combining structural-equation modeling with genomic-relatedness-matrix restricted maximum likelihood in OpenMx. Behav Genet. https://doi.org/10.1007/s10519-020-10037-5

  • Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson BJ, Young AI, Thorgeirsson TE, Benonisdottir S, Oddsson A, Halldorsson BV, Masson G, Gudbjartsson DF, Helgason A, Bjornsdottir G, Thorsteinsdottir U, Stefansson K (2018) The nature of nurture: effects of parental genotypes. Science 359(6374):424–428

    Article  Google Scholar 

  • Lawlor D, Richmond R, Warrington N, McMahon G, Davey Smith G, Bowden J, Evans DM (2017) Using Mendelian randomization to determine causal effects of maternal pregnancy (intrauterine) exposures on offspring outcomes: Sources of bias and methods for assessing them. Wellcome Open Res 2:11

    Article  Google Scholar 

  • Maes HH (2021) Notes on three decades of methodology workshops. Behav Genet. https://doi.org/10.1007/s10519-021-10049-9

  • Moen GH, Hemani G, Warrington NM, Evans DM (2019) Calculating power to detect maternal and offspring genetic effects in genetic association studies. Behav Genet 49(3):327–339

    Article  Google Scholar 

  • Moen GH, Brumpton B, Willer C, Asvold BO, Birkeland KI, Wang G, Neale MC, Freathy RM, Smith GD, Lawlor DA, Kirkpatrick RM, Warrington NM, Evans DM (2020) Mendelian randomization study of maternal influences on birthweight and future cardiometabolic risk in the HUNT cohort. Nat Commun 11(1):5404

    Article  Google Scholar 

  • Neale MC (2006) Mx: statistical modelling, 6th edn. Department of Psychiatry, Richmond

    Google Scholar 

  • Neale MC, Hunter MD, Pritikin JN, Zahery M, Brick TR, Kirkpatrick RM, Estabrook R, Bates TC, Maes HH, Boker SM (2016) OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika 81(2):535–549

    Article  Google Scholar 

  • Pritikin JN, Neale MC, Prom-Wormley EC, Clark SL, Verhulst B (2021) GW-SEM 2.0: efficient, flexible, and accessible multivariate GWAS. Behav Genet. https://doi.org/10.1007/s10519-021-10043-1

  • Qiao Z, Zheng J, Helgeland O, Vaudel M, Johansson S, Njolstad PR, Smith GD, Warrington NM, Evans DM (2020) Introducing M-GCTA a software package to estimate maternal (or paternal) genetic effects on offspring phenotypes. Behav Genet 50(1):51–66

    Article  Google Scholar 

  • Smith GD, Ebrahim S (2003) ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 32(1):1–22

    Article  Google Scholar 

  • Tamimy Z, Kevenaar ST, Hottenga JJ, Hunter MD, de Zeeuw EL, Neale MC, van Beijsterveldt CEM, Dolan CV, van Bergen E, Boomsma DI (2021) Multilevel twin models: geographical region as a third level variable. Behav Genet. https://doi.org/10.1007/s10519-021-10047-x

  • Tubbs JD, Porsch RM, Cherny SS, Sham PC (2020) The genes we inherit and those we don’t: maternal genetic nurture and child BMI trajectories. Behav Genet 50(5):310–319

    Article  Google Scholar 

  • Tubbs JD, Hwang LD, Luong J, Evans DM, Sham PC (2021) Modeling parent-specific genetic nurture in families with missing parental genotypes: application to birthweight and BMI. Behav Genet. https://doi.org/10.1007/s10519-020-10040-w

  • Verhulst B, Neale MC (2021) Best practices for binary and ordinal data analyses. Behav Genet. https://doi.org/10.1007/s10519-020-10031-x

  • Verhulst B, Pritikin JN, Clifford J, Prom-Wormley E (2021) Using genetic marginal effects to study gene-environment interactions with GWAS data. Behav Genet. https://doi.org/10.1007/s10519-021-10058-8

  • Warrington NM, Richmond R, Fenstra B, Myhre R, Gaillard R, Paternoster L, Wang CA, Beaumont RN, Das S, Murcia M, Barton SJ, Espinosa A, Thiering E, Atalay M, Pitkanen N, Ntalla I, Jonsson AE, Freathy R, Karhunen V, Tiesler CMT, Allard C, Crawford A, Ring SM, Melbye M, Magnus P, Rivadeneira F, Skotte L, Hansen T, Marsh J, Guxens M, Holloway JW, Grallert H, Jaddoe VWV, Lowe WL Jr, Roumeliotaki T, Hattersley AT, Lindi V, Pahkala K, Panoutsopoulou K, Standl M, Flexeder C, Bouchard L, Aagaard Nohr E, Marina LS, Kogevinas M, Niinikoski H, Dedoussis G, Heinrich J, Reynolds RM, Lakka T, Zeggini E, Raitakari OT, Chatzi L, Inskip HM, Bustamante M, Hivert MF, Jarvelin MR, Sorensen TIA, Pennell C, Felix JF, Jacobsson B, Geller F, Evans DM, Lawlor DA (2018) Maternal and fetal genetic contribution to gestational weight gain. Int J Obes (Lond) 42(4):775–784

    Article  Google Scholar 

  • Warrington NM, Beaumont RN, Horikoshi M, Day FR, Helgeland O, Laurin C, Bacelis J, Peng S, Hao K, Feenstra B, Wood AR, Mahajan A, Tyrrell J, Robertson NR, Rayner NW, Qiao Z, Moen GH, Vaudel M, Marsit CJ, Chen J, Nodzenski M, Schnurr TM, Zafarmand MH, Bradfield JP, Grarup N, Kooijman MN, Li-Gao R, Geller F, Ahluwalia TS, Paternoster L, Rueedi R, Huikari V, Hottenga JJ, Lyytikainen LP, Cavadino A, Metrustry S, Cousminer DL, Wu Y, Thiering E, Wang CA, Have CT, Vilor-Tejedor N, Joshi PK, Painter JN, Ntalla I, Myhre R, Pitkanen N, van Leeuwen EM, Joro R, Lagou V, Richmond RC, Espinosa A, Barton SJ, Inskip HM, Holloway JW, Santa-Marina L, Estivill X, Ang W, Marsh JA, Reichetzeder C, Marullo L, Hocher B, Lunetta KL, Murabito JM, Relton CL, Kogevinas M, Chatzi L, Allard C, Bouchard L, Hivert MF, Zhang G, Muglia LJ, Heikkinen J, Consortium EGG, Morgen CS, van Kampen AHC, van Schaik BDC, Mentch FD, Langenberg C, Luan J, Scott RA, Zhao JH, Hemani G, Ring SM, Bennett AJ, Gaulton KJ, Fernandez-Tajes J, van Zuydam NR, Medina-Gomez C, de Haan HG, Rosendaal FR, Kutalik Z, Marques-Vidal P, Das S, Willemsen G, Mbarek H, Muller-Nurasyid M, Standl M, Appel EVR, Fonvig CE, Trier C, van Beijsterveldt CEM, Murcia M, Bustamante M, Bonas-Guarch S, Hougaard DM, Mercader JM, Linneberg A, Schraut KE, Lind PA, Medland SE, Shields BM, Knight BA, Chai JF, Panoutsopoulou K, Bartels M, Sanchez F, Stokholm J, Torrents D, Vinding RK, Willems SM, Atalay M, Chawes BL, Kovacs P, Prokopenko I, Tuke MA, Yaghootkar H, Ruth KS, Jones SE, Loh PR, Murray A, Weedon MN, Tonjes A, Stumvoll M, Michaelsen KF, Eloranta AM, Lakka TA, van Duijn CM, Kiess W, Korner A, Niinikoski H, Pahkala K, Raitakari OT, Jacobsson B, Zeggini E, Dedoussis GV, Teo YY, Saw SM, Montgomery GW, Campbell H, Wilson JF, Vrijkotte TGM, Vrijheid M, de Geus E, Hayes MG, Kadarmideen HN, Holm JC, Beilin LJ, Pennell CE, Heinrich J, Adair LS, Borja JB, Mohlke KL, Eriksson JG, Widen EE, Hattersley AT, Spector TD, Kahonen M, Viikari JS, Lehtimaki T, Boomsma DI, Sebert S, Vollenweider P, Sorensen TIA, Bisgaard H, Bonnelykke K, Murray JC, Melbye M, Nohr EA, Mook-Kanamori DO, Rivadeneira F, Hofman A, Felix JF, Jaddoe VWV, Hansen T, Pisinger C, Vaag AA, Pedersen O, Uitterlinden AG, Jarvelin MR, Power C, Hypponen E, Scholtens DM, Lowe WL Jr, Davey Smith G, Timpson NJ, Morris AP, Wareham NJ, Hakonarson H, Grant SFA, Frayling TM, Lawlor DA, Njolstad PR, Johansson S, Ong KK, McCarthy MI, Perry JRB, Evans DM, Freathy RM (2019) Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet 51(5):804–814

    Article  Google Scholar 

  • Wellcome Trust Case Control C (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145):661–678

    Article  Google Scholar 

  • Wu T, Sham PC (2021) On the transformation of genetic effect size from logit to liability scale. Behav Genet. https://doi.org/10.1007/s10519-021-10042-2

  • Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82

    Article  Google Scholar 

  • Yang Q, Millard LAC, Davey Smith G (2020) Proxy gene-by-environment Mendelian randomization study confirms a causal effect of maternal smoking on offspring birthweight, but little evidence of long-term influences on offspring health. Int J Epidemiol 49(4):1207–1218

    Article  Google Scholar 

  • Zhang G, Bacelis J, Lengyel C, Teramo K, Hallman M, Helgeland O, Johansson S, Myhre R, Sengpiel V, Njolstad PR, Jacobsson B, Muglia L (2015) Assessing the causal relationship of maternal height on birth size and gestational age at birth: a Mendelian randomization analysis. PLoS Med 12(8):e1001865

    Article  Google Scholar 

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Funding

D.M.E. and S.E.M are funded by Australian National Health and Medical Research Council Senior Research Fellowships (APP1137714 and APP 1103623).

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Authors and Affiliations

  1. The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia

    David M. Evans

  2. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    David M. Evans

  3. QIMR Berghofer Medical Research Institute, Brisbane, Australia

    Sarah E. Medland

  4. The Division of Epidemiology, Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, USA

    Elizabeth Prom-Wormley

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  1. David M. Evans
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  2. Sarah E. Medland
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  3. Elizabeth Prom-Wormley
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Correspondence to David M. Evans.

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Evans, D.M., Medland, S.E. & Prom-Wormley, E. Introduction to the Special Issue on Statistical Genetic Methods for Human Complex Traits. Behav Genet 51, 165–169 (2021). https://doi.org/10.1007/s10519-021-10057-9

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