Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from GWAS Hits: A Replication of Previous Findings Using Recent Data

Polygenic Scores ◽

Highly Correlated ◽

Using Data ◽

The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment and the largest intelligence GWAS were used to test a polygenic selection model.Weighted and unweighted polygenic scores (PGS) were calculated and compared across populations (N=26) using data from the 1000 Genomes and HGDP-CEPH datasets. A set of 9 SNPs within genomic regions replicated across GWAS publications and a polygenic score calculated from the largest GWAS of educational attainment to date are highly correlated to a previously published factor (r= 0.96). These factors are both highly predictive of average population IQ (r=0.9), and are robust to tests of spatial autocorrelation. Monte Carlo simulations yielded highly significant p values. A subset of SNPs were found in the HGDP-CEPH sample (N= 127). The analysis of this sample yielded a positive correlation with latitude and a low negative correlation with distance from East Africa.This study provides robust results after accounting for spatial autocorrelation with Fst distances and random noise via an empirical Monte Carlo simulation using null SNPs and shows robust reproducibility of results from a previous study.

Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from Gwas Hits: A Replication of Previous Findings Using Recent Data

Psych ◽

10.3390/psychology1010005 ◽

2019 ◽

Vol 1 (1) ◽

pp. 55-75 ◽

Cited By ~ 1

Author(s):

Davide Piffer

Keyword(s):

Monte Carlo ◽

Educational Attainment ◽

Spatial Autocorrelation ◽

Association Studies ◽

Random Noise ◽

Socioeconomic Variables ◽

1000 Genomes ◽

Polygenic Scores ◽

Genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment (EA) were used to test a polygenic selection model. Weighted and unweighted polygenic scores (PGS) were calculated and compared across populations using data from the 1000 Genomes (n = 26), HGDP-CEPH (n = 52) and gnomAD (n = 8) datasets. The PGS from the largest EA GWAS was highly correlated to two previously published PGSs (r = 0.96–0.97, N = 26). These factors are both highly predictive of average population IQ (r = 0.9, N = 23) and Learning index (r = 0.8, N = 22) and are robust to tests of spatial autocorrelation. Monte Carlo simulations yielded highly significant p values. In the gnomAD samples, the correlation between PGS and IQ was almost perfect (r = 0.98, N = 8), and ANOVA showed significant population differences in allele frequencies with positive effect. Socioeconomic variables slightly improved the prediction accuracy of the model (from 78–80% to 85–89%), but the PGS explained twice as much of the variance in IQ compared to socioeconomic variables. In both 1000 Genomes and gnomAD, there was a weak trend for lower GWAS significance SNPs to be less predictive of population IQ. Additionally, a subset of SNPs were found in the HGDP-CEPH sample (N = 127). The analysis of this sample yielded a positive correlation with latitude and a low negative correlation with distance from East Africa. This study provides robust results after accounting for spatial autocorrelation with Fst distances and random noise via an empirical Monte Carlo simulation using null SNPs.

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

10.20944/preprints201701.0127.v3 ◽

2017 ◽

Author(s):

Davide Piffer

Keyword(s):

Monte Carlo ◽

Educational Attainment ◽

Spatial Autocorrelation ◽

Predictive Power ◽

Association Studies ◽

Null Model ◽

Polygenic Score ◽

Polygenic Scores ◽

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.

Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from Gwas Hits: A Replication of Previous Findings Using Recent Data

Psych ◽

10.3390/psych1010005 ◽

2019 ◽

Vol 1 (1) ◽

pp. 55-75 ◽

Cited By ~ 6

Author(s):

Davide Piffer

Keyword(s):

Monte Carlo ◽

Educational Attainment ◽

Spatial Autocorrelation ◽

Association Studies ◽

Random Noise ◽

Socioeconomic Variables ◽

1000 Genomes ◽

Polygenic Scores ◽

Genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment (EA) were used to test a polygenic selection model. Weighted and unweighted polygenic scores (PGS) were calculated and compared across populations using data from the 1000 Genomes (n = 26), HGDP-CEPH (n = 52) and gnomAD (n = 8) datasets. The PGS from the largest EA GWAS was highly correlated to two previously published PGSs (r = 0.96–0.97, N = 26). These factors are both highly predictive of average population IQ (r = 0.9, N = 23) and Learning index (r = 0.8, N = 22) and are robust to tests of spatial autocorrelation. Monte Carlo simulations yielded highly significant p values. In the gnomAD samples, the correlation between PGS and IQ was almost perfect (r = 0.98, N = 8), and ANOVA showed significant population differences in allele frequencies with positive effect. Socioeconomic variables slightly improved the prediction accuracy of the model (from 78–80% to 85–89%), but the PGS explained twice as much of the variance in IQ compared to socioeconomic variables. In both 1000 Genomes and gnomAD, there was a weak trend for lower GWAS significance SNPs to be less predictive of population IQ. Additionally, a subset of SNPs were found in the HGDP-CEPH sample (N = 127). The analysis of this sample yielded a positive correlation with latitude and a low negative correlation with distance from East Africa. This study provides robust results after accounting for spatial autocorrelation with Fst distances and random noise via an empirical Monte Carlo simulation using null SNPs.

Can We Detect Polygenic Selection on Cognitive Ability Using GWAS Hits? Employing Random SNPs as a Null Model.

10.20944/preprints201701.0127.v2 ◽

2017 ◽

Author(s):

Davide Piffer

Keyword(s):

Educational Attainment ◽

Spatial Autocorrelation ◽

Predictive Power ◽

Association Studies ◽

Null Model ◽

Polygenic Scores ◽

Using Data ◽

Genomic Regions ◽

Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model.ethods: 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=0.9), 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 (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.

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

10.20944/preprints201611.0047.v1 ◽

2016 ◽

Author(s):

Davide Piffer

Keyword(s):

Educational Attainment ◽

Cognitive Ability ◽

Association Studies ◽

Meta Analysis ◽

Significant Snps ◽

P Value ◽

Model Average ◽

Polygenic Scores ◽

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.

Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down

Current Directions in Psychological Science ◽

10.1177/0963721418807729 ◽

2019 ◽

Vol 28 (1) ◽

pp. 82-90 ◽

Cited By ~ 18

Author(s):

Daniel W. Belsky ◽

K. Paige Harden

Keyword(s):

Educational Attainment ◽

Association Studies ◽

Science Research ◽

Well Being ◽

Genome Wide Association ◽

Research Activities ◽

Genome Wide ◽

Polygenic Scores ◽

Methodological Considerations

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

Learning polygenic scores for human blood cell traits

10.1101/2020.02.17.952788 ◽

2020 ◽

Author(s):

Yu Xu ◽

Dragana Vuckovic ◽

Scott C Ritchie ◽

Parsa Akbari ◽

Tao Jiang ◽

...

Keyword(s):

Machine Learning ◽

Blood Cell ◽

Association Studies ◽

Relative Effectiveness ◽

Univariate Analysis ◽

Genetic Correlations ◽

Learning Methods ◽

Polygenic Scores ◽

Using Data

AbstractPolygenic scores (PGSs) for blood cell traits can be constructed using summary statistics from genome-wide association studies. As the selection of variants and the modelling of their interactions in PGSs may be limited by univariate analysis, therefore, such a conventional method may yield sub-optional performance. This study evaluated the relative effectiveness of four machine learning and deep learning methods, as well as a univariate method, in the construction of PGSs for 26 blood cell traits, using data from UK Biobank (n=~400,000) and INTERVAL (n=~40,000). Our results showed that learning methods can improve PGSs construction for nearly every blood cell trait considered, with this superiority explained by the ability of machine learning methods to capture interactions among variants. This study also demonstrated that populations can be well stratified by the PGSs of these blood cell traits, even for traits that exhibit large differences between ages and sexes, suggesting potential for disease prevention. As our study found genetic correlations between the PGSs for blood cell traits and PGSs for several common human diseases (recapitulating well-known associations between the blood cell traits themselves and certain diseases), it suggests that blood cell traits may be indicators or/and mediators for a variety of common disorders via shared genetic variants and functional pathways.

Genomic prediction of cognitive traits in childhood and adolescence

10.1101/418210 ◽

2018 ◽

Cited By ~ 4

Author(s):

A.G. Allegrini ◽

S. Selzam ◽

K. Rimfeld ◽

S. von Stumm ◽

J.B. Pingault ◽

...

Keyword(s):

Educational Attainment ◽

Complex Traits ◽

Predictive Power ◽

Prediction Models ◽

Educational Achievement ◽

Association Studies ◽

Occupational Status ◽

Childhood And Adolescence ◽

Polygenic Scores

AbstractRecent 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.

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

10.20944/preprints201701.0127.v1 ◽

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.