@article {271, title = {Evidence for gene-environment correlation in child feeding: Links between common genetic variation for BMI in children and parental feeding practices}, journal = {PLOS Genetics}, volume = {14}, year = {2018}, month = {11}, pages = {1-19}, abstract = {

The parental feeding practices (PFPs) of excessive restriction of food intake (\‘restriction\’) and pressure to increase food consumption (\‘pressure\’) have been argued to causally influence child weight in opposite directions (high restriction causing overweight; high pressure causing underweight). However child weight could also \‘elicit\’ PFPs. A novel approach is to investigate gene-environment correlation between child genetic influences on BMI and PFPs. Genome-wide polygenic scores (GPS) combining BMI-associated variants were created for 10,346 children (including 3,320 DZ twin pairs) from the Twins Early Development Study using results from an independent genome-wide association study meta-analysis. Parental \‘restriction\’ and \‘pressure\’ were assessed using the Child Feeding Questionnaire. Child BMI standard deviation scores (BMI-SDS) were calculated from children\’s height and weight at age 10. Linear regression and fixed family effect models were used to test between- (n = 4,445 individuals) and within-family (n = 2,164 DZ pairs) associations between the GPS and PFPs. In addition, we performed multivariate twin analyses (n = 4,375 twin pairs) to estimate the heritabilities of PFPs and the genetic correlations between BMI-SDS and PFPs. The GPS was correlated with BMI-SDS (β = 0.20, p = 2.41x10-38). Consistent with the gene-environment correlation hypothesis, child BMI GPS was positively associated with \‘restriction\’ (β = 0.05, p = 4.19x10-4), and negatively associated with \‘pressure\’ (β = -0.08, p = 2.70x10-7). These results remained consistent after controlling for parental BMI, and after controlling for overall family contributions (within-family analyses). Heritabilities for \‘restriction\’ (43\% [40\–47\%]) and \‘pressure\’ (54\% [50\–59\%]) were moderate-to-high. Twin-based genetic correlations were moderate and positive between BMI-SDS and \‘restriction\’ (rA = 0.28 [0.23\–0.32]), and substantial and negative between BMI-SDS and \‘pressure\’ (rA = -0.48 [-0.52 - -0.44]. Results suggest that the degree to which parents limit or encourage children\’s food intake is partly influenced by children\’s genetic predispositions to higher or lower BMI. These findings point to an evocative gene-environment correlation in which heritable characteristics in the child elicit parental feeding behaviour.

}, doi = {10.1371/journal.pgen.1007757}, url = {https://doi.org/10.1371/journal.pgen.1007757}, author = {Saskia Selzam and McAdams, Tom A. and Jonathan R I Coleman and Carnell, Susan and Paul F O{\textquoteright}Reilly and Robert Plomin and Llewellyn, Clare H.} } @article {200, title = {Genome-Wide Polygenic Scores Predict Reading Performance Throughout the School Years}, journal = {Scientific Studies of Reading}, volume = {21}, year = {2017}, month = {2017/07/04}, pages = {334 - 349}, abstract = {

It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education (EduYears) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5\% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5\% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education.

}, isbn = {1088-84381532-799X}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490720/}, author = {Saskia Selzam and Philip S. Dale and Wagner, Richard K and John C DeFries and Cederl{\"o}f, Martin and Paul F O{\textquoteright}Reilly and Eva Krapohl and Robert Plomin} } @article {199, title = {Predicting educational achievement from DNA}, journal = {Mol Psychiatry}, volume = {22}, year = {2017}, month = {2017/02//print}, pages = {267 - 272}, abstract = {

A genome-wide polygenic score (GPS), derived from a 2013 genome-wide association study (N=127,000), explained 2\% of the variance in total years of education (EduYears). In a follow-up study (N=329,000), a new EduYears GPS explains up to 4\%. Here, we tested the association between this latest EduYears GPS and educational achievement scores at ages 7, 12 and 16 in an independent sample of 5825 UK individuals. We found that EduYears GPS explained greater amounts of variance in educational achievement over time, up to 9\% at age 16, accounting for 15\% of the heritable variance. This is the strongest GPS prediction to date for quantitative behavioral traits. Individuals in the highest and lowest GPS septiles differed by a whole school grade at age 16. Furthermore, EduYears GPS was associated with general cognitive ability (~3.5\%) and family socioeconomic status (~7\%). There was no evidence of an interaction between EduYears GPS and family socioeconomic status on educational achievement or on general cognitive ability. These results are a harbinger of future widespread use of GPS to predict genetic risk and resilience in the social and behavioral sciences.

}, isbn = {1359-4184}, url = {http://dx.doi.org/10.1038/mp.2016.107}, author = {Saskia Selzam and Eva Krapohl and Sophie von Stumm and Paul F O{\textquoteright}Reilly and Kaili Rimfeld and Yulia Kovas and Philip S. Dale and Lee,J J and Robert Plomin} } @article {270, title = {Widespread covariation of early environmental exposures and trait-associated polygenic variation}, journal = {Proceedings of the National Academy of Sciences}, volume = {114}, year = {2017}, pages = {11727{\textendash}11732}, abstract = {

Environmental exposures are among the best predictors of health and educational outcomes. Models that estimate the effect of environmental exposures on developmental outcomes typically ignore genetic factors or focus on gene\–environment interaction (whether individuals\’ response to environmental exposures depends on their genotype). Here we test gene\–environment correlation (whether individuals\’ exposure to environments depends on their genotype). Using a method that tests specific genetic effects while controlling for background genetic effects, we estimate covariation between children\’s genetic liability/propensity for core developmental outcomes and a wide range of environmental exposures. Findings suggest that genetic variants associated with traits, such as educational attainment, body mass index, and schizophrenia, also capture environmental risk and protective factors.Although gene\–environment correlation is recognized and investigated by family studies and recently by SNP-heritability studies, the possibility that genetic effects on traits capture environmental risk factors or protective factors has been neglected by polygenic prediction models. We investigated covariation between trait-associated polygenic variation identified by genome-wide association studies (GWASs) and specific environmental exposures, controlling for overall genetic relatedness using a genomic relatedness matrix restricted maximum-likelihood model. In a UK-representative sample (n = 6,710), we find widespread covariation between offspring trait-associated polygenic variation and parental behavior and characteristics relevant to children\’s developmental outcomes\—independently of population stratification. For instance, offspring genetic risk for schizophrenia was associated with paternal age (R2 = 0.002; P = 1e-04), and offspring education-associated variation was associated with variance in breastfeeding (R2 = 0.021; P = 7e-30), maternal smoking during pregnancy (R2 = 0.008; P = 5e-13), parental smacking (R2 = 0.01; P = 4e-15), household income (R2 = 0.032; P = 1e-22), watching television (R2 = 0.034; P = 5e-47), and maternal education (R2 = 0.065; P = 3e-96). Education-associated polygenic variation also captured covariation between environmental exposures and children\’s inattention/hyperactivity, conduct problems, and educational achievement. The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

}, issn = {0027-8424}, doi = {10.1073/pnas.1707178114}, url = {https://www.pnas.org/content/114/44/11727}, author = {Eva Krapohl and Hannigan, L. J. and Pingault, J.-B. and Patel, H. and Kadeva, N. and Curtis, C. and Breen, G. and Newhouse, S. J. and Thalia C. Eley and Paul F O{\textquoteright}Reilly and Robert Plomin} }