Biology subjects

Rooney, W.

Publications and source records attributed to Rooney, W..

3 recordsLinked to original sources

bioRxiv2023

Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments

For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across- and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low GxE. A genome-wide association study (GWAS) implicated known (i.e., MADS69, ZCN8, sh1, wx1, du1) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key messageGenomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.

plant biology↗

bioRxiv2021

Pregnancy and weaning regulate human maternal liver size and function

BACKGROUNDDuring pregnancy, the rodent liver undergoes hepatocyte proliferation and increases in size, followed by weaning-induced involution via hepatocyte cell death and stromal remodeling, creating a pro-metastatic niche. These data suggest a mechanism for increased liver metastasis in postpartum breast cancer patients. OBJECTIVESInvestigate if the human liver changes in size and function during pregnancy and weaning. METHODSAbdominal imaging was obtained in healthy women at early and late pregnancy, and post-wean. During pregnancy time points, endogenous glucose production was measured and fasting blood taken to measure bile acids. RESULTSIndependent of weight gain, most womens livers increased in size with pregnancy, returning to baseline post-wean. Putative roles for bile acids in liver growth were observed in pregnant women and rodents. CONCLUSIONSThe human liver is regulated by reproductive state with growth during pregnancy and volume loss post-wean. These findings may have broad implications for sex-specific liver diseases and cancer.

physiology↗

bioRxiv2017

An automated statistical technique for counting distinct multiple sclerosis lesions can recover aspects of lesion history and provide relevant disease information

BackgroundLesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest associations with clinical outcomes. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (i.e. spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions.\n\nMethodsMRI is used to assess the probability of lesion at each location. The texture of this map is quantified using a novel technique, and clusters resembling the center of a lesion are counted.\n\nResultsValidity was demonstrated by comparing the proposed count to a gold-standard count in 60 subjects observed longitudinally. The counts were highly correlated (r = .97, p < .001) and not significantly different (t59 = -0.83, p > .40). Reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites, and variability of lesion count was equivalent to that of lesion load. Accounting for lesion load and age, lesion count was negatively associated (t58 = -2.73, p < .01) with the Expanded Disability Status Scale (EDSS). Average lesion size had a higher association with EDSS (r =.35, p < .01) than lesion load (r = .10, p > .40) or lesion count (r = -.12, p > .30) alone.\n\nConclusionThese findings demonstrate that it is possible to recover important aspects of the natural history of lesion formation without longitudinal data, and suggest that lesion size provides complementary information about disease.\n\nGrant SupportThe project described was supported in part by the NIH grants R01 NS085211, R21 NS093349, and R01 NS094456 from the National Institute of Neurological Disorders and Stroke (NINDS). The study was also supported by the Intramural Research Program of NINDS and the Race to Erase MS Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

neuroscience↗