Biology subjects

Selzam, S.

Publications and source records attributed to Selzam, S..

10 recordsLinked to original sources

bioRxiv2022

Genomic contributions to infant and toddler vocabulary scores: Implications for association with health-, cognition- and behaviour-related outcomes

BackgroundThe number of words children produce (expressive vocabulary) and understand (receptive vocabulary) changes rapidly during early development, partially due to genetic factors, although mechanisms are not well understood. Here, we performed a meta-genome-wide association study within the EAGLE consortium and investigated polygenic overlap with later-life traits, including Attention-Deficit/Hyperactivity Disorder (ADHD) and cognition. MethodsWe studied 37,913 parent-reported vocabulary size measures (English, Dutch, Danish) for 17,298 children of European descent. Meta-analyses were performed for early-phase expressive (infancy, 15-18 months), late-phase expressive (toddlerhood, 24-38 months) and late-phase receptive (toddlerhood, 24-38 months) vocabulary. Subsequently, we estimated Single-Nucleotide Polymorphism heritability (SNP-h2), genetic correlations (rg) and modelled underlying genetic factor structures with multivariate models. ResultsContributions of common genetic variation to early-life vocabulary were modest (SNP-h2: 0.08(SE=0.01) to 0.24(SE=0.03)) and multi-factorial. Genetic overlap between infant expressive and toddler receptive vocabulary was near zero (rg=0.07(SE=0.10)), although both measures were genetically related to toddler expressive vocabulary (rg=0.69(SE=0.14) and rg=0.67(SE=0.16), respectively). Consistently, polygenic association patterns with later-life traits differed: Genetic links with cognition emerged only in toddlerhood (e.g. toddler receptive vocabulary and intelligence: rg=0.36(SE=0.12)), despite comparable study power for infant measures. Furthermore, increased polygenic ADHD risk was associated with larger infant expressive vocabulary (rg=0.23(SE=0.08)), as confirmed by ADHD-symptom-based follow-up analyses in the Avon Longitudinal Study of Parents and Children (ALSPAC-rg=0.54(SE=0.26)). Genetic relationships with toddler receptive vocabulary were, however, opposite (ALSPAC-rg=-0.74(SE=0.23)), highlighting developmental changes in genetic architectures. ConclusionsMultiple genetic components contribute to early-life vocabulary development, shaping polygenic association patterns with later-life ADHD symptoms and cognition.

genomics↗

bioRxiv2020

Imputed Gene Expression Risk Scores: A Functionally Informed Component of Polygenic Risk

BackgroundIntegration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. MethodsThe predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study (TEDS). GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed SNP-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. ResultsGeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for Height (p=0.023) to 4% for Rheumatoid Arthritis (p=5.9x10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalised genes. ConclusionGeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

genomics↗

bioRxiv2019

Multivariable G-E interplay in the prediction of educational achievement

Polygenic scores are increasingly powerful predictors of educational achievement. It is unclear, however, how sets of polygenic scores, which partly capture environmental effects, perform jointly with sets of environmental measures, which are themselves heritable, in prediction models of educational achievement. Here, for the first time, we systematically investigate gene-environment correlation (rGE) and interaction (GxE) in the joint analysis of multiple genome-wide polygenic scores (GPS) and multiple environmental measures as they predict tested educational achievement (EA). We predict EA in a representative sample of 7,026 16-year-olds, with 20 GPS for psychiatric, cognitive and anthropometric traits, and 13 environments (including life events, home environment, and SES) measured earlier in life. Environmental and GPS predictors were modelled, separately and jointly, in penalized regression models with out-of-sample comparisons of prediction accuracy, considering the implications that their interplay had on model performance. Jointly modelling multiple GPS and environmental factors significantly improved prediction of EA, with cognitive-related GPS adding unique independent information beyond SES, home environment and life events. We found evidence for rGE underlying variation in EA (rGE = .36; 95% CIs = .29, .43). We estimated that 38% (95% CIs = 29%, 49%) of the GPS effects on EA were mediated by environmental effects, and in turn that 18% (95% CIs =12%, 25%) of environmental effects were accounted for by the GPS model. Lastly, we did not find evidence that GxE effects collectively contributed to multivariable prediction. Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence.

genetics↗

bioRxiv2019

Comparing within- and between-family polygenic score prediction

Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight life outcomes (anthropometric, cognitive, personality and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modelling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a source of between-family prediction through rGE mechanisms. These results provide novel insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.

genomics↗

bioRxiv2019

Estimating the sensitivity of associations between risk factors and outcomes to shared genetic effects

Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes - child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely. Author summaryAn objective shared across the life, behavioural, and social sciences is to identify factors that increase risk for a particular disease or trait. However, identifying true risk factors is challenging. Often, a risk factor is statistically associated with a disease even if it is not really relevant, meaning that even successfully improving the risk factor will not impact the disease. One reason for the existence of such misleading associations stems from genetic confounding. This is when genetic factors influence both the risk factor and the disease, which generates a statistical association even in the absence of a true effect of the risk factor. Here, we propose a method to estimate genetic confounding and quantify its effect on observed associations. We show that a large part of the associations between maternal education and three child outcomes - educational achievement, body mass index and Attention-Deficit Hyperactivity Disorder-is explained by genetic confounding. Our findings can be applied to better understand the role of genetics in explaining associations of key risk factors with diseases and traits.

genetics↗

bioRxiv2019

The p factor: Genetic analyses support a general dimension of psychopathology in childhood and adolescence

BackgroundDiverse behaviour problems in childhood correlate phenotypically, suggesting a general dimension of psychopathology that has been called the p factor. The shared genetic architecture between childhood psychopathology traits also supports a genetic p. This study systematically investigates the manifestation of this common dimension across self-, parent- and teacher-rated measures in childhood and adolescence.\n\nMethodsThe sample included 7,026 twin pairs from the Twins Early Development Study (TEDS). First, we employed multivariate twin models to estimate common genetic and environmental influences on p based on diverse measures of behaviour problems rated by children, parents and teachers at ages 7, 9, 12 and 16 (depressive symptoms, emotional problems, peer problems, autistic symptoms, hyperactivity, antisocial, conduct and psychopathic symptoms). Second, to assess the stability of genetic and environmental influences on p across time, we conducted longitudinal twin modelling of the first phenotypic principal components of childhood psychopathological measures across each of the four ages. Third, we created a genetic p factor in 7,026 unrelated genotyped individuals based on eight polygenic scores for adult psychiatric disorders to estimate how a general polygenic predisposition to adult psychiatric disorders relates to childhood p.\n\nResultsBehaviour problems were consistently correlated phenotypically and genetically across ages and raters. The p factor is substantially heritable (50-60%), and manifests consistently across diverse ages and raters. Genetic correlations of p components across childhood and adolescence suggest stability over time (49-78%). A polygenic general psychopathology factor, derived from studies of adult psychiatric disorders consistently predicted a general phenotypic p factor across development.\n\nConclusionsDiverse forms of psychopathology consistently load on a common p factor, which is highly heritable. There are substantial genetic influences on the stability of p across childhood. Our analyses indicate genetic overlap between general risk for psychiatric disorders in adulthood and p in childhood, even as young as age 7. The p factor has far-reaching implications for genomic research and, eventually, for diagnosis and treatment of behaviour problems.

genetics↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗