| Title: | Genotype-environment interaction on human cognitive function conditioned on the status of breastfeeding and maternal smoking around birth |
| Journal: | Scientific Reports |
| Published: | 20 Jul 2017 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/28729621/ |
| DOI: | https://doi.org/10.1038/s41598-017-06214-y |
| URL: | https://www.nature.com/articles/s41598-017-06214-y.pdf |
Publication 8478
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Abstract
We estimated genotype by environment interaction (G × E) on later cognitive performance and educational attainment across four unique environments, i.e. 1) breastfed without maternal smoking, 2) breastfed with maternal smoking, 3) non-breastfed without maternal smoking and 4) non-breastfed with maternal smoking, using a novel design and statistical approach that was facilitated by the availability of datasets with the genome-wide single nucleotide polymorphisms (SNPs). There was significant G × E for both fluid intelligence (p-value = 1.0E-03) and educational attainment (p-value = 8.3E-05) when comparing genetic effects in the group of individuals who were breastfed without maternal smoking with those not breastfed without maternal smoking. There was also significant G × E for fluid intelligence (p-value = 3.9E-05) when comparing the group of individuals who were breastfed with maternal smoking with those not breastfed without maternal smoking. Genome-wide significant SNPs were different between different environmental groups. Genomic prediction accuracies were significantly higher when using the target and discovery sample from the same environmental group than when using those from the different environmental groups. This finding demonstrates G × E has important implications for future studies on the genetic architecture, genome-wide association studies and genomic predictions.</p>
3 Authors
- S. Hong Lee
- W. M. Shalanee P. Weerasinghe
- Julius H. J. van der Werf
1 Application
| Application ID | Title |
|---|---|
| 14575 | Whole-genome approaches for dissecting (shared) genetic architecture and individual risk prediction of complex traits in human populations |
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