Predicting educational achievement from genomic measures and socioeconomic status

Sophie von Stumm; Emily Smith-Woolley; Ziada Ayorech; Andrew McMillan; Kaili Rimfeld; Philip S. Dale; Robert Plomin

doi:10.1111/desc.12925

Predicting educational achievement from genomic measures and socioeconomic status

Sophie von Stumm^*, Emily Smith-Woolley, Ziada Ayorech, Andrew McMillan, Kaili Rimfeld, Philip S. Dale, Robert Plomin

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

86 Citations (Scopus)

Abstract

The two best predictors of children's educational achievement available from birth are parents’ socioeconomic status (SES) and, recently, children's inherited DNA differences that can be aggregated in genome-wide polygenic scores (GPS). Here, we chart for the first time the developmental interplay between these two predictors of educational achievement at ages 7, 11, 14 and 16 in a sample of almost 5,000 UK school children. We show that the prediction of educational achievement from both GPS and SES increases steadily throughout the school years. Using latent growth curve models, we find that GPS and SES not only predict educational achievement in the first grade but they also account for systematic changes in achievement across the school years. At the end of compulsory education at age 16, GPS and SES, respectively, predict 14% and 23% of the variance of educational achievement. Analyses of the extremes of GPS and SES highlight their influence and interplay: In children who have high GPS and come from high SES families, 77% go to university, whereas 21% of children with low GPS and from low SES backgrounds attend university. We find that the associations of GPS and SES with educational achievement are primarily additive, suggesting that their joint influence is particularly dramatic for children at the extreme ends of the distribution.

Original language	English
Article number	e12925
Journal	Developmental Science
DOIs	https://doi.org/10.1111/desc.12925
Publication status	Published - 1 Jan 2019

Keywords

educational achievement
gene–environment interplay
genome-wide polygenic scores
longitudinal
socioeconomic status

Access to Document

10.1111/desc.12925

Cite this

@article{4753a8a5933c4dedb35103ba5a97d4d3,

title = "Predicting educational achievement from genomic measures and socioeconomic status",

abstract = "The two best predictors of children's educational achievement available from birth are parents{\textquoteright} socioeconomic status (SES) and, recently, children's inherited DNA differences that can be aggregated in genome-wide polygenic scores (GPS). Here, we chart for the first time the developmental interplay between these two predictors of educational achievement at ages 7, 11, 14 and 16 in a sample of almost 5,000 UK school children. We show that the prediction of educational achievement from both GPS and SES increases steadily throughout the school years. Using latent growth curve models, we find that GPS and SES not only predict educational achievement in the first grade but they also account for systematic changes in achievement across the school years. At the end of compulsory education at age 16, GPS and SES, respectively, predict 14% and 23% of the variance of educational achievement. Analyses of the extremes of GPS and SES highlight their influence and interplay: In children who have high GPS and come from high SES families, 77% go to university, whereas 21% of children with low GPS and from low SES backgrounds attend university. We find that the associations of GPS and SES with educational achievement are primarily additive, suggesting that their joint influence is particularly dramatic for children at the extreme ends of the distribution.",

keywords = "educational achievement, gene–environment interplay, genome-wide polygenic scores, longitudinal, socioeconomic status",

author = "{von Stumm}, Sophie and Emily Smith-Woolley and Ziada Ayorech and Andrew McMillan and Kaili Rimfeld and Dale, {Philip S.} and Robert Plomin",

year = "2019",

month = jan,

day = "1",

doi = "10.1111/desc.12925",

language = "English",

journal = "Developmental Science",

issn = "1363-755X",

publisher = "Wiley-Blackwell",

}

TY - JOUR

T1 - Predicting educational achievement from genomic measures and socioeconomic status

AU - von Stumm, Sophie

AU - Smith-Woolley, Emily

AU - Ayorech, Ziada

AU - McMillan, Andrew

AU - Rimfeld, Kaili

AU - Dale, Philip S.

AU - Plomin, Robert

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The two best predictors of children's educational achievement available from birth are parents’ socioeconomic status (SES) and, recently, children's inherited DNA differences that can be aggregated in genome-wide polygenic scores (GPS). Here, we chart for the first time the developmental interplay between these two predictors of educational achievement at ages 7, 11, 14 and 16 in a sample of almost 5,000 UK school children. We show that the prediction of educational achievement from both GPS and SES increases steadily throughout the school years. Using latent growth curve models, we find that GPS and SES not only predict educational achievement in the first grade but they also account for systematic changes in achievement across the school years. At the end of compulsory education at age 16, GPS and SES, respectively, predict 14% and 23% of the variance of educational achievement. Analyses of the extremes of GPS and SES highlight their influence and interplay: In children who have high GPS and come from high SES families, 77% go to university, whereas 21% of children with low GPS and from low SES backgrounds attend university. We find that the associations of GPS and SES with educational achievement are primarily additive, suggesting that their joint influence is particularly dramatic for children at the extreme ends of the distribution.

AB - The two best predictors of children's educational achievement available from birth are parents’ socioeconomic status (SES) and, recently, children's inherited DNA differences that can be aggregated in genome-wide polygenic scores (GPS). Here, we chart for the first time the developmental interplay between these two predictors of educational achievement at ages 7, 11, 14 and 16 in a sample of almost 5,000 UK school children. We show that the prediction of educational achievement from both GPS and SES increases steadily throughout the school years. Using latent growth curve models, we find that GPS and SES not only predict educational achievement in the first grade but they also account for systematic changes in achievement across the school years. At the end of compulsory education at age 16, GPS and SES, respectively, predict 14% and 23% of the variance of educational achievement. Analyses of the extremes of GPS and SES highlight their influence and interplay: In children who have high GPS and come from high SES families, 77% go to university, whereas 21% of children with low GPS and from low SES backgrounds attend university. We find that the associations of GPS and SES with educational achievement are primarily additive, suggesting that their joint influence is particularly dramatic for children at the extreme ends of the distribution.

KW - educational achievement

KW - gene–environment interplay

KW - genome-wide polygenic scores

KW - longitudinal

KW - socioeconomic status

UR - http://www.scopus.com/inward/record.url?scp=85077062397&partnerID=8YFLogxK

U2 - 10.1111/desc.12925

DO - 10.1111/desc.12925

M3 - Article

AN - SCOPUS:85077062397

SN - 1363-755X

JO - Developmental Science

JF - Developmental Science

M1 - e12925

ER -

Predicting educational achievement from genomic measures and socioeconomic status

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this