KAUST Grant NumberFCS/1/4102- 02-01
Permanent link to this recordhttp://hdl.handle.net/10754/673941
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AbstractThe human sex ratio at birth (SRB), defined as the ratio between the number of newborn boys to the total number of newborns, is typically slightly greater than 1/2 (more boys than girls) and tends to vary across different geographical regions and time periods. In this large-scale study, we sought to validate previously-reported associations and test new hypotheses using statistical analysis of two very large datasets incorporating electronic medical records (EMRs). One of the datasets represents over half (∼ 150 million) of the US population for over 8 years (IBM Watson Health MarketScan insurance claims) while another covers the entire Swedish population (∼ 9 million) for over 30 years (the Swedish National Patient Register). After testing more than 100 hypotheses, we showed that neither dataset supported models in which the SRB changed seasonally or in response to variations in ambient temperature. However, increased levels of a diverse array of air and water pollutants, were associated with lower SRBs, including increased levels of industrial and agricultural activity, which served as proxies for water pollution. Moreover, some exogenous factors generally considered to be environmental toxins turned out to induce higher SRBs. Finally, we identified new factors with signals for either higher or lower SRBs. In all cases, the effect sizes were modest but highly statistically significant owing to the large sizes of the two datasets. We suggest that while it was unlikely that the associations have arisen from sex-specific selection mechanisms, they are still useful for the purpose of public health surveillance if they can be corroborated by empirical evidences.
CitationLong, Y., Chen, Q., Larsson, H., & Rzhetsky, A. (2021). Observable variations in human sex ratio at birth. PLOS Computational Biology, 17(12), e1009586. doi:10.1371/journal.pcbi.1009586
SponsorsWe are grateful to E. Gannon and M. Rzhetsky for comments on earlier versions of this manuscript.
Funding: A.R. was funded by the DARPA Big Mechanism program under ARO contract W911NF1410333, by National Institutes of Health grants R01HL122712, 1P50MH094267, and U01HL108634-01, and by a gift from Liz and Kent Dauten. Additional support for A.R. came from King Abdullah University of Science and Technology (KAUST), awards number FCS/1/4102- 02-01, FCC/1/1976-26-01, REI/1/0018-01-01, and REI/1/4473-01-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
PublisherPublic Library of Science (PLoS)
JournalPLOS Computational Biology