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Items: 1 to 20 of 111

1.

What is the biological reality of gene-environment interaction estimates? An assessment of bias in developmental models.

Moore SR, Thoemmes F.

J Child Psychol Psychiatry. 2016 Nov;57(11):1258-1267. doi: 10.1111/jcpp.12579. Epub 2016 May 31.

PMID:
27240973
2.

Allowing for population stratification in case-only studies of gene-environment interaction, using genomic control.

Yadav P, Freitag-Wolf S, Lieb W, Dempfle A, Krawczak M.

Hum Genet. 2015 Oct;134(10):1117-25. doi: 10.1007/s00439-015-1593-y. Epub 2015 Aug 22.

PMID:
26297539
3.

A latent variable approach to study gene-environment interactions in the presence of multiple correlated exposures.

Sánchez BN, Kang S, Mukherjee B.

Biometrics. 2012 Jun;68(2):466-76. doi: 10.1111/j.1541-0420.2011.01677.x. Epub 2011 Sep 28.

4.

Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle.

Tiezzi F, de Los Campos G, Parker Gaddis KL, Maltecca C.

J Dairy Sci. 2017 Mar;100(3):2042-2056. doi: 10.3168/jds.2016-11543. Epub 2017 Jan 18.

6.

Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures.

Tao Y, Sánchez BN, Mukherjee B.

Stat Med. 2015 Mar 30;34(7):1227-41. doi: 10.1002/sim.6401. Epub 2014 Dec 29.

7.

Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution.

Keller MC.

Biol Psychiatry. 2014 Jan 1;75(1):18-24. doi: 10.1016/j.biopsych.2013.09.006. Epub 2013 Oct 15. Review.

8.

Alternating optimization for G × E modelling with weighted genetic and environmental scores: Examples from the MAVAN study.

Jolicoeur-Martineau A, Wazana A, Szekely E, Steiner M, Fleming AS, Kennedy JL, Meaney MJ, Greenwood CMT.

Psychol Methods. 2019 Apr;24(2):196-216. doi: 10.1037/met0000175. Epub 2018 Aug 13.

PMID:
30102054
9.

Assessing genotype by environment interaction in case of heterogeneous measurement error.

Schwabe I, van den Berg SM.

Behav Genet. 2014 Jul;44(4):394-406. doi: 10.1007/s10519-014-9649-7. Epub 2014 Mar 20.

PMID:
24647833
10.

Genotype by production environment interaction for birth and weaning weights in a population of composite beef cattle.

Santana ML Jr, Eler JP, Bignardi AB, Ferraz JB.

Animal. 2014 Mar;8(3):379-87. doi: 10.1017/S1751731113002255.

PMID:
24534687
11.

Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults.

Wolock SL, Yates A, Petrill SA, Bohland JW, Blair C, Li N, Machiraju R, Huang K, Bartlett CW.

J Child Psychol Psychiatry. 2013 Oct;54(10):1109-19. doi: 10.1111/jcpp.12119. Epub 2013 Aug 2.

12.

Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype.

Gagneur J, Stegle O, Zhu C, Jakob P, Tekkedil MM, Aiyar RS, Schuon AK, Pe'er D, Steinmetz LM.

PLoS Genet. 2013;9(9):e1003803. doi: 10.1371/journal.pgen.1003803. Epub 2013 Sep 19.

13.

Causal effects on child language development: A review of studies in communication sciences and disorders.

Rogers CR, Nulty KL, Betancourt MA, DeThorne LS.

J Commun Disord. 2015 Sep-Oct;57:3-15. doi: 10.1016/j.jcomdis.2015.06.004. Epub 2015 Jul 2. Review.

PMID:
26255254
14.

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids.

Acosta-Pech R, Crossa J, de Los Campos G, Teyssèdre S, Claustres B, Pérez-Elizalde S, Pérez-Rodríguez P.

Theor Appl Genet. 2017 Jul;130(7):1431-1440. doi: 10.1007/s00122-017-2898-0. Epub 2017 Apr 11.

PMID:
28401254
15.

Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models.

Mota RR, Tempelman RJ, Lopes PS, Aguilar I, Silva FF, Cardoso FF.

Genet Sel Evol. 2016 Jan 14;48:3. doi: 10.1186/s12711-015-0178-5.

16.

Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL.

Sohns M, Viktorova E, Amos CI, Brennan P, Fehringer G, Gaborieau V, Han Y, Heinrich J, Chang-Claude J, Hung RJ, Müller-Nurasyid M, Risch A, Thomas D, Bickeböller H.

Genet Epidemiol. 2013 Sep;37(6):551-559. doi: 10.1002/gepi.21741. Epub 2013 Jul 26.

17.

The impact of exposure-biased sampling designs on detection of gene-environment interactions in case-control studies with potential exposure misclassification.

Stenzel SL, Ahn J, Boonstra PS, Gruber SB, Mukherjee B.

Eur J Epidemiol. 2015 May;30(5):413-23. doi: 10.1007/s10654-014-9908-1. Epub 2014 Jun 4.

18.

Summary of relationships between exchangeability, biasing paths and bias.

Flanders WD, Eldridge RC.

Eur J Epidemiol. 2015 Oct;30(10):1089-99. doi: 10.1007/s10654-014-9915-2. Epub 2014 Jun 4. Review.

PMID:
24894825
19.

Environmental confounding in gene-environment interaction studies.

Vanderweele TJ, Ko YA, Mukherjee B.

Am J Epidemiol. 2013 Jul 1;178(1):144-52. doi: 10.1093/aje/kws439. Epub 2013 May 21.

20.

Analysis of Behavioral and Emotional Problems in Children Highlights the Role of Genotype × Environment Interaction.

Molenaar D, Middeldorp C, van Beijsterveldt T, Boomsma DI.

Child Dev. 2015 Nov-Dec;86(6):1999-2016. doi: 10.1111/cdev.12451. Epub 2015 Oct 28.

PMID:
26509842

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