| Title: | Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders |
| Journal: | Human Molecular Genetics |
| Published: | 4 Mar 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33682876/ |
| DOI: | https://doi.org/10.1093/hmg/ddab060 |
| URL: | https://academic.oup.com/hmg/article-pdf/30/11/1057/39630164/ddab060.pdf |
| Citations: | 2 (1 in last 2 years) as of 8 Aug 2024 |
Publication 4560
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Abstract
Birth weight is an important factor in newborn survival; both low and high birth weights are associated with adverse later-life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with maternal or fetal effects on birth weight. Knowledge of the underlying causal genes is crucial to understand how these loci influence birth weight and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme ends of the distribution. Genes implicated in those syndromes may provide valuable information to prioritize candidate genes at the GWAS loci. We examined the proximity of genes implicated in developmental disorders (DDs) to birth weight GWAS loci using simulations to test whether they fall disproportionately close to the GWAS loci. We found birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected both when the DD gene is the nearest gene to the birth weight SNP and also when examining all genes within 258 kb of the SNP. This enrichment was driven by genes causing monogenic DDs with dominant modes of inheritance. We found examples of SNPs in the intron of one gene marking plausible effects via different nearby genes, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight, which has helped identify GWAS loci likely to have direct fetal effects on birth weight, which could not previously be classified as fetal or maternal owing to insufficient statistical power.</p>
4 Authors
- Robin N Beaumont
- Isabelle K Mayne
- Rachel M Freathy
- Caroline F Wright
Enabling scientific discoveries that improve human health