Association between low fetal fraction in cell-free DNA screening and fetal chromosomal aberrations: A systematic review and meta-analysis

Objective

To perform a systematic review and meta-analysis of the available literature on low fetal fraction (LFF) in cell-free DNA (cfDNA) screening and the risk of fetal chromosomal aberrations.

Method

We searched articles published between January 2010 and May 2021 in PubMed and EMBASE databases. Risk of bias was assessed using QUADAS-2.

Results

Twenty-seven studies met the inclusion criteria, comprising data of 243,700 singleton pregnancies. Compared to normal fetal fraction, LFF was associated with a higher risk of trisomy 13 (OR 5.99 [3.61–9.95], I ² of heterogeneity = 0%, n = 22 studies), trisomy 18 (OR 4.46 [3.07–6.47], I ² = 0%, n = 22 studies), monosomy X (OR 5.88 [2.34–14.78], I ² = 18%, n = 10 studies), and triploidy (OR 36.39 [9.83–134.68], I ² = 61%, n = 6 studies), but not trisomy 21 (OR 1.25 [0.76–2.03], I ² = 36%, n = 23 studies). LFF was also associated with a higher risk of various other types of fetal chromosomal aberrations (OR 4.00 [1.78–9.00], I ² = 2%, n = 11 studies). Meta-analysis of proportions showed that absolute rates of fetal chromosomal aberrations ranged between 1% and 2% in women with LFF. A limitation of this review is the potential risk of ascertainment bias because of differences in outcome assessment between pregnancies with LFF and those with normal fetal fraction. Heterogeneity in population characteristics or applied technologies across included studies may not have been fully addressed.

Conclusion

An LFF test result in cfDNA screening is associated with an increased risk of fetal trisomy 13, trisomy 18, monosomy X, and triploidy, but not trisomy 21. Further research is needed to assess the association between LFF and other specific types of fetal chromosomal aberrations.     

Modelling habitat overlap among sympatric mesocarnivores in southern Illinois,USA

Few researchers have developed large-scale habitat models for sympatric carnivore species. We created habitat models for red foxes (Vulpes vulpes), coyotes (Canis latrans) and bobcats (Lynx rufus) in southern Illinois, USA, using the Penrose distance statistic, remotely sensed landscape data, and sighting location data within a GIS. Our objectives were to quantify and spatially model potential habitat differences among species. Habitat variables were quantified for 1-km² buffered areas around mesocarnivore sighting locations. Following variable reduction procedures, five habitat variables (percentage of grassland patches, interspersion–juxtaposition of forest patches, mean fractal dimension of wetland patches and the landscape, and road density) were used for analysis. Only one variable differed (P < 0.05) between red fox and coyote sighting areas (road density) and bobcat and coyote sighting areas (mean fractal dimension of the landscape). However, all five variables differed between red fox and bobcat sighting areas, indicating considerable differences in habitat affiliation between this pair-group. Compared to bobcats, red fox sightings were affiliated with more grassland cover and larger grassland patches, higher road densities, lower interspersion and juxtaposition of forest patches, and lower mean fractal dimension of wetland patches. These differences can be explained by different life history requirements relative to specific cover types. We then used the Penrose distance statistic to create habitat models for red foxes and bobcats, respectively, based on the five-variable dataset. An independent set of sighting locations were used to validate these models; model fit was good with 65% of mesocarnivore locations within the top 50% of Penrose distance values. In general, red foxes were affiliated with mixtures of agricultural and grassland cover, whereas bobcats were associated with a combination of grassland, wetland, and forest cover. The greatest habitat overlap between red foxes and bobcats was found at the interface between forested areas and more open cover types. Our study provides insight into habitat overlap among sympatric mesocarnivores, and the distance-based modelling approach we used has numerous applications for modelling wildlife–habitat relationships over large scales.