scholarly journals The nature of nurture: effects of parental genotypes

2017 ◽  
Author(s):  
Augustine Kong ◽  
Gudmar Thorleifsson ◽  
Michael L. Frigge ◽  
Bjarni J. Vilhjálmsson ◽  
Alexander I. Young ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Meta Analysis ◽  
Genetic Component ◽  
Sequence Variants ◽  
Genetic Studies ◽  
Polygenic Score ◽  
Polygenic Scores

AbstractSequence variants in the parental genomes that are not transmitted to a child/proband are often ignored in genetic studies. Here we show that non-transmitted alleles can impact a child through their effects on the parents and other relatives, a phenomenon we call genetic nurture. Using results from a meta-analysis of educational attainment, the polygenic score computed for the non-transmitted alleles of 21,637 probands with at least one parent genotyped has an estimated effect on the educational attainment of the proband that is 29.9% (P = 1.6×10−14) of that of the transmitted polygenic score. Genetic nurturing effects of this polygenic score extend to other traits. Paternal and maternal polygenic scores have similar effects on educational attainment, but mothers contribute more than fathers to nutrition/heath related traits.One Sentence SummaryNurture has a genetic component, i.e. alleles in the parents affect the parents’ phenotypes and through that influence the outcomes of the child.

scholarly journals Structural Brain MRI Trait Polygenic Score Prediction of Cognitive Abilities

2015 ◽  
Vol 18 (6) ◽  
pp. 738-745 ◽  
Author(s):  
Michelle Luciano ◽  
Riccardo E. Marioni ◽  
Maria Valdés Hernández ◽  
Susana Muñoz Maniega ◽  
Iona F. Hamilton ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Brain Mri ◽  
Phenotypic Traits ◽  
Intracranial Volume ◽  
Birth Cohorts ◽  
Polygenic Score ◽  
Cognitive Measure ◽  
Polygenic Scores ◽  
Cognitive Traits ◽  
Structural Brain

Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association (GWA) for brain infarcts (BI), white matter hyperintensities, intracranial, hippocampal, and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to: (1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits), and (2) predict cognitive traits in all three cohorts (in 8,115–8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure, and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r = 0.08) between the HV polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the GWA samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies.

scholarly journals Evidence for Recent Polygenic Selection on Educational Attainment and Underlying Cognitive Abilities Inferred from GWAS Hits: A Monte Carlo Simulation Using Random SNPs

2017 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Monte Carlo ◽  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Predictive Power ◽  
Association Studies ◽  
Null Model ◽  
Genome Wide Association Studies ◽  
Polygenic Score ◽  
Polygenic Scores ◽  
Polygenic Selection

Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model. Methods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. A null model was created using frequencies of random SNPs. Results: Polygenic selection signal of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications. A polygenic score comprising 9 SNPs predicts population IQ (r=0.88), outperforming 99% of the polygenic scores obtained from sets of random SNPs (Monte Carlo p= 0.011). Its predictive power remains unaffected after controlling for spatial autocorrelation (Beta= 0.83). The largest polygenic score (161 SNPs) exhibits similar predictive power (Beta=0.8). Random polygenic scores are moderate predictors of population IQ (thanks to spatial autocorrelation), and their predictive power increases logarithmically with the number of SNPs, indicating an exponential reduction in noise. Conclusion: This study provides guidance for using GWAS hits together with random SNPs for testing polygenic selection using Monte Carlo simulations.

scholarly journals Evidence for Recent Polygenic Selection on Educational Attainment Inferred from GWAS Hits

2016 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Educational Attainment ◽  
Cognitive Ability ◽  
Association Studies ◽  
Meta Analysis ◽  
Significant Snps ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Model Average ◽  
Polygenic Scores ◽  
Polygenic Selection

The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model. Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. The PS of 161 GWAS significant SNPs in a recent meta-analysis was highly correlated to population IQ (r=0.863) and to the polygenic score of four alleles independently associated with general cognitive ability. High  correlations with PISA scores for a subsample were observed.SNP p value predicted correlation to population IQ and factors from the two previous GWAS (r= -.25). Factor analysis produced similar estimates of selection pressure for educational attainment across the three datasets. Polygenic and factor scores computed using the top 20 significant SNPs showed very high correlation to population IQ (r=0.88; 0.9). Similar findings were obtained using 52 populations from another database (ALFRED). The results together constitute a replication of preliminary findings and provide strong evidence for recent diversifying polygenic selection on educational attainment and underlying cognitive ability.

scholarly journals Stop Meta-Analyzing, Start Instrumenting: Maximizing the Predictive Power of Polygenic Scores

2021 ◽  
Author(s):  
Hans van Kippersluis ◽  
Pietro Biroli ◽  
Titus J. Galama ◽  
Stephanie von Hinke ◽  
S. Fleur W. Meddens ◽  
...  
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Predictive Power ◽  
Association Studies ◽  
Meta Analysis ◽  
Ordinary Least Squares ◽  
Genome Wide Association Studies ◽  
Finite Sample ◽  
Polygenic Score ◽  
Polygenic Scores

Polygenic scores have become the workhorse for empirical analyses in social-science genetics. Because a polygenic score is constructed using the results of finite-sample Genome-Wide Association Studies (GWASs), it is a noisy approximation of the true latent genetic predisposition to a certain trait. The conventional way of boosting the predictive power of polygenic scores is to increase the GWAS sample size by meta-analyzing GWAS results of multiple cohorts. In this paper we challenge this convention. Through simulations, we show that Instrumental Variable (IV) regression using two polygenic scores from independent GWAS samples outperforms the typical Ordinary Least Squares (OLS) model employing a single meta-analysis based polygenic score in terms of bias, root mean squared error, and statistical power. We verify the empirical validity of these simulations by predicting educational attainment (EA) and height in a sample of siblings from the UK Biobank. We show that IV regression between-families approaches the SNP-based heritabilities, while compared to meta-analysis applying IV regression within-families provides a tighter lower bound on the direct genetic effect. IV estimation improves the predictive power of polygenic scores by 12% (height) to 22% (EA). Our findings suggest that measurement error is a key explanation for hidden heritability (i.e., the difference between SNP-based and GWAS-based heritability), and that it can be overcome using IV regression. We derive the practical rule of thumb that IV outperforms OLS when the correlation between the two polygenic scores used in IV regression is larger than √(10 / (N+10)), with N the sample size of the prediction sample.

scholarly journals Polygenic Selection, Polygenic Scores, Spatial Autocorrelation and Correlated Allele Frequencies. Can We Model Polygenic Selection on Intellectual Abilities?

2017 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Predictive Power ◽  
Allele Frequencies ◽  
Polygenic Score ◽  
Polygenic Scores ◽  
Intellectual Abilities ◽  
Genomic Regions ◽  
Polygenic Selection ◽  
Selection Signal

The majority of polygenic selection signal of educational attainment GWAS hits is confined to a handful of SNPs within genomic regions replicated across GWAS publications. A polygenic score comprising 9 SNPs predicts population IQ (r=0.9), outperforming 99.9% of the polygenic scores obtained from sets of random SNPs. Its predictive power remains unaffected after controlling for spatial autocorrelation. Even random polygenic scores are moderate predictors of population IQ, and their predictive power increases logarithmically with the number of SNPs, indicating an exponential reduction in noise.Thus, the predictive power of polygenic scores has to be scaled in proportion to the number of SNPs composing them.

scholarly journals Genetic effects on educational attainment in Hungary

2020 ◽  
Author(s):  
Péter P. Ujma ◽  
Nóra Eszlári ◽  
András Millinghoffer ◽  
Bence Bruncsics ◽  
Péter Petschner ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Genetic Variants ◽  
Educational Level ◽  
Statistical Power ◽  
Association Studies ◽  
Molecular Genetic ◽  
Genetic Effects ◽  
Genome Wide Association Studies ◽  
Genetic Studies ◽  
Polygenic Scores

AbstractEducational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, the effects of such genetic variants are expected to vary across environments, including countries and historical eras. We used polygenic scores (PGSs) to assess molecular genetic effects on educational attainment in Hungary, a country in the Central Eastern European region where behavioral genetic studies are in general scarce and molecular genetic studies of educational attainment have not been previously published. We found that the PGS is significantly associated with highest educational level attained as well as the number of years in education in a sample of Hungarian volunteers (N=829). In an English (N=976) comparison sample with identical measurement protocols the same PGS had a stronger association with educational level, but not with years in education. In line with previous Estonian findings, we found higher PGS effect sizes in Hungarian, but not in English participants who attended higher education after the fall of Communism, although we lacked statistical power for this effect to reach significance. Our results provide evidence that polygenic scores for educational attainment are valid in diverse European populations.

scholarly journals Educational Attainment and Personality Are Genetically Intertwined

2017 ◽  
Vol 28 (11) ◽  
pp. 1631-1639 ◽  
Author(s):  
René Mõttus ◽  
Anu Realo ◽  
Uku Vainik ◽  
Jüri Allik ◽  
Tõnu Esko
Keyword(s):  
Educational Attainment ◽  
Personality Traits ◽  
Personality Trait ◽  
Factor Model ◽  
Five Factor Model ◽  
Molecular Genetic ◽  
Maximum Amount ◽  
Biological Processes ◽  
Psychological Traits ◽  
Polygenic Scores

Heritable variance in psychological traits may reflect genetic and biological processes that are not necessarily specific to these particular traits but pertain to a broader range of phenotypes. We tested the possibility that the personality domains of the five-factor model and their 30 facets, as rated by people themselves and their knowledgeable informants, reflect polygenic influences that have been previously associated with educational attainment. In a sample of more than 3,000 adult Estonians, education polygenic scores (EPSs), which are interpretable as estimates of molecular-genetic propensity for education, were correlated with various personality traits, particularly from the neuroticism and openness domains. The correlations of personality traits with phenotypic educational attainment closely mirrored their correlations with EPS. Moreover, EPS predicted an aggregate personality trait tailored to capture the maximum amount of variance in educational attainment almost as strongly as it predicted the attainment itself. We discuss possible interpretations and implications of these findings.

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