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

2017 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Predictive Power ◽  
Association Studies ◽  
Null Model ◽  
Genome Wide Association Studies ◽  
Polygenic Scores ◽  
Using Data ◽  
Genomic Regions ◽  
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.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.

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

Psych ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 55-75 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Monte Carlo ◽  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Association Studies ◽  
Random Noise ◽  
Genome Wide Association Studies ◽  
Socioeconomic Variables ◽  
1000 Genomes ◽  
Polygenic Scores ◽  
Polygenic Selection

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 intelligence inferred from GWAS hits: a replication of previous findings using recent data.

2018 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Monte Carlo ◽  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Association Studies ◽  
Random Noise ◽  
Genome Wide Association Studies ◽  
Polygenic Scores ◽  
Highly Correlated ◽  
Using Data ◽  
Polygenic Selection

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.

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

2017 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Monte Carlo ◽  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Association Studies ◽  
Selection Model ◽  
Genome Wide Association Studies ◽  
Polygenic Scores ◽  
Highly Correlated ◽  
Using Data ◽  
Polygenic Selection

Background: 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. Methods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. Factor analysis was used to extract a signal of polygenic selection. Results: A polygenic selection factor of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications and it is highly correlated to the genetic intelligence 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. Several Monte Carlo simulations yielded highly significant p values. Furthermore, the polygenic selection model shows high replicability, with the EA and intelligence factor scores being virtually identical to those from an older study (r=0.96-0.99). A larger sample of populations (N=53) produced similar results. Conclusion: This study shows robust results after accounting for spatial autocorrelation and Monte Carlo simulation using random SNPs and shows robust reproducibility of results from a previous study.

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

Psych ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 55-75 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Monte Carlo ◽  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Association Studies ◽  
Random Noise ◽  
Genome Wide Association Studies ◽  
Socioeconomic Variables ◽  
1000 Genomes ◽  
Polygenic Scores ◽  
Polygenic Selection

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.

scholarly journals Support for the genetic hypothesis of the Black-white achievement gap using polygenic scores and tests for divergent selection

2020 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Educational Attainment ◽  
Cognitive Performance ◽  
Genetic Variants ◽  
Race Differences ◽  
Divergent Evolution ◽  
Math Ability ◽  
Gwas Snps ◽  
Polygenic Scores ◽  
Polygenic Selection ◽  
Genetic Hypothesis

Using the latest methods to detect divergent evolution and polygenic selection, I test the hypothesis that race differences (European-African) in IQ are due to genetic differences.The genetic variants identified by the largest GWAS of education showed clear signatures of differentiation between Africans and Europeans. Across different phenotypes (educational attainment, cognitive performance, math ability), GWAS SNPs had significantly higher average Fst than control SNPs. Contrary to a previous report, the same effect was found also for a GWAS based on a within-family design, that used differences in educational attainment between siblings to partial out shared environmental effects. Polygenic scores for all phenotypes and GWAS types (including within-family design) were higher for Europeans than for Africans.

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 Genomic prediction of cognitive traits in childhood and adolescence

2018 ◽  
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 ◽  
Genome Wide Association Studies ◽  
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.

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