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Sökning: WFRF:(Moen Thomas) > (2020-2023)

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1.
  • Beaumont, Robin N, et al. (författare)
  • Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth.
  • 2023
  • Ingår i: Nature genetics. - 1546-1718 .- 1061-4036. ; 55:11, s. 1807-19
  • Tidskriftsartikel (refereegranskat)abstract
    • A well-functioning placenta is essential for fetal and maternal health throughout pregnancy. Using placental weight as a proxy for placental growth, we report genome-wide association analyses in the fetal (n=65,405), maternal (n=61,228) and paternal (n=52,392) genomes, yielding 40 independent association signals. Twenty-six signals are classified as fetal, four maternal and three fetal and maternal. A maternal parent-of-origin effect is seen near KCNQ1. Genetic correlation and colocalization analyses reveal overlap with birth weight genetics, but 12 loci are classified as predominantly or only affecting placental weight, with connections to placental development and morphology, and transport of antibodies and amino acids. Mendelian randomization analyses indicate that fetal genetically mediated higher placental weight is causally associated with preeclampsia risk and shorter gestational duration. Moreover, these analyses support the role of fetal insulin in regulating placental weight, providing a key link between fetal and placental growth.
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2.
  • Grabowski, Mark, et al. (författare)
  • A Cautionary Note on "A Cautionary Note on the Use of Ornstein Uhlenbeck Models in Macroevolutionary Studies"
  • 2023
  • Ingår i: SYSTEMATIC BIOLOGY. - 1063-5157 .- 1076-836X. ; 72:4, s. 955-963
  • Tidskriftsartikel (refereegranskat)abstract
    • Models based on the Ornstein-Uhlenbeck process have become standard for the comparative study of adaptation. have cast doubt on this practice by claiming statistical problems with fitting Ornstein-Uhlenbeck models to comparative data. Specifically, they claim that statistical tests of Brownian motion may have too high Type I error rates and that such error rates are exacerbated by measurement error. In this note, we argue that these results have little relevance to the estimation of adaptation with Ornstein-Uhlenbeck models for three reasons. First, we point out that did not consider the detection of distinct optima (e.g. for different environments), and therefore did not evaluate the standard test for adaptation. Second, we show that consideration of parameter estimates, and not just statistical significance, will usually lead to correct inferences about evolutionary dynamics. Third, we show that bias due to measurement error can be corrected for by standard methods. We conclude that have not identified any statistical problems specific to Ornstein-Uhlenbeck models, and that their cautions against their use in comparative analyses are unfounded and misleading.
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