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Selzam, S.

Publications and source records attributed to Selzam, S..

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Genomic prediction of cognitive traits in childhood and adolescence

Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at age 12 and 16, we show that we can now predict up to 11 percent of the variance in intelligence and 16 percent in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. Multivariate genomic methods were effective in boosting predictive power and, even though prediction accuracy varied across polygenic scores approaches, results were similar using different multivariate and polygenic score methods. Polygenic scores for educational attainment and intelligence are the most powerful predictors in the behavioural sciences and exceed predictions that can be made from parental phenotypes such as educational attainment and occupational status.

genomics

Evidence for gene-environment correlation in child feeding: Links between common genetic variation for BMI in children and parental feeding practices

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 childrens 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 ({beta}=0.20, p=2.41x10-38). Consistent with the gene-environment correlation hypothesis, child BMI GPS was positively associated with restriction ({beta}=0.05, p=4.19x10-4), and negatively associated with pressure ({beta}=-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 childrens food intake is partly influenced by childrens 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.\n\nAuthor SummaryIt is widely believed that parents influence their childs BMI via certain feeding practices. For example, rigid restriction has been argued to cause overweight, and pressuring to eat to cause underweight. However, recent longitudinal research has not supported this model. An alternative hypothesis is that child BMI, which has a strong genetic basis, evokes parental feeding practices ( gene-environment correlation). To test this, we applied two genetic methods in a large sample of 10-year-old children from the Twins Early Development Study: a polygenic score analysis (DNA-based score of common genetic variants robustly associated with BMI in genome-wide meta-analyses), and a twin analysis (comparing resemblance between identical and non-identical twin pairs). Polygenic scores correlated positively with parental restriction of food intake ( restriction; {beta}=0.05, p=4.19x10-4), and negatively with parental pressure to increase food intake ( pressure; {beta}=-0.08, p=2.70x10-7). Associations were unchanged after controlling for all genetic and environmental effects shared within families. Results from twin analyses were consistent. Restriction (43%) and pressure (54%) were substantially heritable, and a positive genetic correlation between child BMI and restriction (rA=0.28), and negative genetic correlation between child BMI and pressure (rA=-0.48) emerged. These findings challenge the prevailing view that parental behaviours are the sole cause of child BMI by supporting an alternate hypothesis that child BMI also causes parental feeding behaviour.

genomics

Genes associated with neuropsychiatric disease increase vulnerability to abnormal deep grey matter development

1.BackgroundNeuropsychiatric disease has polygenic determinants but is often precipitated by environmental pressures, including adverse perinatal events. However, the way in which genetic vulnerability and early-life adversity interact remains obscure. Preterm birth is associated with abnormal brain development and psychiatric disease. We hypothesised that the extreme environmental stress of premature extra-uterine life could contribute to neuroanatomic abnormality in genetically vulnerable individuals.\n\nMethodsWe combined Magnetic Resonance Imaging (MRI) and genome-wide single nucleotide polymorphism (SNP) data from 194 infants, born before 33 weeks of gestation, to test the prediction that: the characteristic deep grey matter abnormalities seen in preterm infants are associated with polygenic risk for psychiatric illness. Summary statistics from a meta-analysis of SNP data for five psychiatric disorders were used to compute individual polygenic risk scores (PRS). The variance explained by the PRS in the relative volumes of four deep grey matter structures (caudate nucleus, thalamus, subthalamic nucleus and lentiform nucleus) was estimated using linear regression both for the full, mixed-ancestral, cohort and a subsample of European infants.\n\nResultsThe PRS was negatively associated with: lentiform volume in the full cohort ({beta}=-0.24, p=8x10-4) and the European subsample ({beta}=-0.24, p=8x10-3); and with subthalamic nuclear volume in the full cohort ({beta}=-0.18, p=0.01) and the European subsample ({beta}=-0.26, p=3x10-3).\n\nConclusionsGenetic variants associated with neuropsychiatric disease increase vulnerability to abnormal deep grey matter development and are associated with neuroanatomic changes in the perinatal period. This suggests a mechanism by which perinatal adversity leads to later neuropsychiatric disease in genetically predisposed individuals.

neuroscience

A polygenic p factor for major psychiatric disorders

It has recently been proposed that a single dimension, called the p factor, can capture a persons liability to mental disorder. Relevant to the p hypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by using principal component analysis on matrices of genetic correlations between major psychiatric disorders estimated by three methods - family study, Genome-wide Complex Trait Analysis, and Linkage-Disequilibrium Score Regression - and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7,026 unrelated individuals. All disorders loaded on a first unrotated principal component, which accounted for 57%, 43%, 34% and 19% of the variance respectively for each method. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.

genomics